{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/pose-estimation/papers/11","list_of":"/task/pose-estimation","task":"Pose Estimation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":11,"pages_in_order":43,"rows_per_page":100,"rows":[1001,1100],"of":4228,"counts":{"archive_papers_tagged":4228,"with_a_code_link":1679,"where_syntology_ran_a_sample":376,"not_listed_spam_title":0,"listed":4228,"listed_where_code_ran":376,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":327,"every_run_a_failure_of_syntologys_instrument":49,"listed_with_a_run_with_no_instrument_failure":327,"listed_every_run_a_failure_of_syntologys_instrument":49,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/pose-estimation","prev":"/task/pose-estimation/papers/10","next":"/task/pose-estimation/papers/12","papers":[{"url":"/paper/approximate-differentiable-rendering-with","slug":"approximate-differentiable-rendering-with","title":"Approximate Differentiable Rendering with Algebraic Surfaces","date":"2022-07-21","arxiv_id":"2207.10606","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/approximate-differentiable-rendering-with#ran","syntology_url":"https://syntology.ai/paper/2207.10606","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10606"}},"official":{"repos":["leonidk/fuzzy-metaballs"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/meshloc-mesh-based-visual-localization","slug":"meshloc-mesh-based-visual-localization","title":"MeshLoc: Mesh-Based Visual Localization","date":"2022-07-21","arxiv_id":"2207.10762","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/meshloc-mesh-based-visual-localization#ran","syntology_url":"https://syntology.ai/paper/2207.10762","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10762"}},"official":{"repos":["tsattler/meshloc_release"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pose-for-everything-towards-category-agnostic","slug":"pose-for-everything-towards-category-agnostic","title":"Pose for Everything: Towards Category-Agnostic Pose Estimation","date":"2022-07-21","arxiv_id":"2207.10387","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pose-for-everything-towards-category-agnostic#ran","syntology_url":"https://syntology.ai/paper/2207.10387","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10387"}},"official":{"repos":["luminxu/pose-for-everything"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/brace-the-breakdancing-competition-dataset","slug":"brace-the-breakdancing-competition-dataset","title":"BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis","date":"2022-07-20","arxiv_id":"2207.10120","repositories_listed":1,"syntology":null},{"url":"/paper/otpose-occlusion-aware-transformer-for-pose","slug":"otpose-occlusion-aware-transformer-for-pose","title":"OTPose: Occlusion-Aware Transformer for Pose Estimation in Sparsely-Labeled Videos","date":"2022-07-20","arxiv_id":"2207.09725","repositories_listed":1,"syntology":null},{"url":"/paper/virtualpose-learning-generalizable-3d-human","slug":"virtualpose-learning-generalizable-3d-human","title":"VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data","date":"2022-07-20","arxiv_id":"2207.09949","repositories_listed":1,"syntology":null},{"url":"/paper/computer-vision-to-the-rescue-infant-postural","slug":"computer-vision-to-the-rescue-infant-postural","title":"Computer Vision to the Rescue: Infant Postural Symmetry Estimation from Incongruent Annotations","date":"2022-07-19","arxiv_id":"2207.09352","repositories_listed":1,"syntology":null},{"url":"/paper/dh-aug-dh-forward-kinematics-model-driven","slug":"dh-aug-dh-forward-kinematics-model-driven","title":"DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose Estimation","date":"2022-07-19","arxiv_id":"2207.09303","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":5,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/dh-aug-dh-forward-kinematics-model-driven#ran","syntology_url":"https://syntology.ai/paper/2207.09303","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.09303"}},"official":{"repos":["hlz0606/dh-aug-dh-forward-kinematics-model-driven-augmentation-for-3d-human-pose-estimation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/particlesfm-exploiting-dense-point","slug":"particlesfm-exploiting-dense-point","title":"ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild","date":"2022-07-19","arxiv_id":"2207.09137","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/particlesfm-exploiting-dense-point#ran","syntology_url":"https://syntology.ai/paper/2207.09137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.09137"}},"official":{"repos":["bytedance/particle-sfm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/posernet-refining-relative-camera-poses","slug":"posernet-refining-relative-camera-poses","title":"PoserNet: Refining Relative Camera Poses Exploiting Object Detections","date":"2022-07-19","arxiv_id":"2207.09445","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/posernet-refining-relative-camera-poses#ran","syntology_url":"https://syntology.ai/paper/2207.09445","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.09445"}},"official":{"repos":["iit-pavis/posernet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deflowslam-self-supervised-scene-motion","slug":"deflowslam-self-supervised-scene-motion","title":"D$^3$FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance","date":"2022-07-18","arxiv_id":"2207.08794","repositories_listed":1,"syntology":null},{"url":"/paper/catre-iterative-point-clouds-alignment-for","slug":"catre-iterative-point-clouds-alignment-for","title":"CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement","date":"2022-07-17","arxiv_id":"2207.08082","repositories_listed":1,"syntology":null},{"url":"/paper/ca-spacenet-counterfactual-analysis-for-6d","slug":"ca-spacenet-counterfactual-analysis-for-6d","title":"CA-SpaceNet: Counterfactual Analysis for 6D Pose Estimation in Space","date":"2022-07-16","arxiv_id":"2207.07869","repositories_listed":1,"syntology":null},{"url":"/paper/nefsac-neurally-filtered-minimal-samples","slug":"nefsac-neurally-filtered-minimal-samples","title":"NeFSAC: Neurally Filtered Minimal Samples","date":"2022-07-16","arxiv_id":"2207.07872","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/nefsac-neurally-filtered-minimal-samples#ran","syntology_url":"https://syntology.ai/paper/2207.07872","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.07872"}},"official":{"repos":["cavalli1234/nefsac"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/transgrasp-grasp-pose-estimation-of-a","slug":"transgrasp-grasp-pose-estimation-of-a","title":"TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance","date":"2022-07-16","arxiv_id":"2207.07861","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":1,"n_ran_checked":2,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"6 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/transgrasp-grasp-pose-estimation-of-a#ran","syntology_url":"https://syntology.ai/paper/2207.07861","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.07861"}},"official":{"repos":["yanjh97/transgrasp"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/adversarial-attacks-on-monocular-pose","slug":"adversarial-attacks-on-monocular-pose","title":"Adversarial Attacks on Monocular Pose Estimation","date":"2022-07-14","arxiv_id":"2207.07032","repositories_listed":1,"syntology":null},{"url":"/paper/corri2p-deep-image-to-point-cloud","slug":"corri2p-deep-image-to-point-cloud","title":"CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence","date":"2022-07-12","arxiv_id":"2207.05483","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-estimate-external-forces-of-human","slug":"learning-to-estimate-external-forces-of-human","title":"Learning to Estimate External Forces of Human Motion in Video","date":"2022-07-12","arxiv_id":"2207.05845","repositories_listed":1,"syntology":null},{"url":"/paper/snipper-a-spatiotemporal-transformer-for","slug":"snipper-a-spatiotemporal-transformer-for","title":"Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet","date":"2022-07-09","arxiv_id":"2207.04320","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-human-pose-estimation-in-art","slug":"semi-supervised-human-pose-estimation-in-art","title":"Semi-supervised Human Pose Estimation in Art-historical Images","date":"2022-07-06","arxiv_id":"2207.02976","repositories_listed":1,"syntology":null},{"url":"/paper/pvo-panoptic-visual-odometry","slug":"pvo-panoptic-visual-odometry","title":"PVO: Panoptic Visual Odometry","date":"2022-07-04","arxiv_id":"2207.01610","repositories_listed":1,"syntology":null},{"url":"/paper/how-far-can-i-go-a-self-supervised-approach","slug":"how-far-can-i-go-a-self-supervised-approach","title":"How Far Can I Go ? : A Self-Supervised Approach for Deterministic Video Depth Forecasting","date":"2022-07-01","arxiv_id":"2207.00506","repositories_listed":1,"syntology":null},{"url":"/paper/hm3d-abo-a-photo-realistic-dataset-for-object","slug":"hm3d-abo-a-photo-realistic-dataset-for-object","title":"HM3D-ABO: A Photo-realistic Dataset for Object-centric Multi-view 3D Reconstruction","date":"2022-06-24","arxiv_id":"2206.12356","repositories_listed":1,"syntology":null},{"url":"/paper/optimal-and-robust-category-level-perception","slug":"optimal-and-robust-category-level-perception","title":"Optimal and Robust Category-level Perception: Object Pose and Shape Estimation from 2D and 3D Semantic Keypoints","date":"2022-06-24","arxiv_id":"2206.12498","repositories_listed":1,"syntology":null},{"url":"/paper/promptpose-language-prompt-helps-animal-pose","slug":"promptpose-language-prompt-helps-animal-pose","title":"CLAMP: Prompt-based Contrastive Learning for Connecting Language and Animal Pose","date":"2022-06-23","arxiv_id":"2206.11752","repositories_listed":1,"syntology":null},{"url":"/paper/i-2r-net-intra-and-inter-human-relation","slug":"i-2r-net-intra-and-inter-human-relation","title":"I^2R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation","date":"2022-06-22","arxiv_id":"2206.10892","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-of-image-scale-and","slug":"self-supervised-learning-of-image-scale-and","title":"Self-Supervised Learning of Image Scale and Orientation","date":"2022-06-15","arxiv_id":"2206.07259","repositories_listed":1,"syntology":{"n":17,"n_ran":16,"n_constructed":0,"n_ran_checked":16,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":0,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-learning-of-image-scale-and#ran","syntology_url":"https://syntology.ai/paper/2206.07259","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.07259"}},"official":{"repos":["bluedream1121/self-sca-ori"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/trihorn-net-a-model-for-accurate-depth-based","slug":"trihorn-net-a-model-for-accurate-depth-based","title":"TriHorn-Net: A Model for Accurate Depth-Based 3D Hand Pose Estimation","date":"2022-06-14","arxiv_id":"2206.07117","repositories_listed":1,"syntology":null},{"url":"/paper/graphmlp-a-graph-mlp-like-architecture-for-3d","slug":"graphmlp-a-graph-mlp-like-architecture-for-3d","title":"GraphMLP: A Graph MLP-Like Architecture for 3D Human Pose Estimation","date":"2022-06-13","arxiv_id":"2206.06420","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/graphmlp-a-graph-mlp-like-architecture-for-3d#ran","syntology_url":"https://syntology.ai/paper/2206.06420","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.06420"}},"official":{"repos":["vegetebird/graphmlp"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/e-2-pn-efficient-se-3-equivariant-point","slug":"e-2-pn-efficient-se-3-equivariant-point","title":"E2PN: Efficient SE(3)-Equivariant Point Network","date":"2022-06-11","arxiv_id":"2206.05398","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/e-2-pn-efficient-se-3-equivariant-point#ran","syntology_url":"https://syntology.ai/paper/2206.05398","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.05398"}},"official":{"repos":["minghanz/e2pn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-human-pose-estimation-via-3d-event","slug":"efficient-human-pose-estimation-via-3d-event","title":"Efficient Human Pose Estimation via 3D Event Point Cloud","date":"2022-06-09","arxiv_id":"2206.04511","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/efficient-human-pose-estimation-via-3d-event#ran","syntology_url":"https://syntology.ai/paper/2206.04511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.04511"}},"official":{"repos":["masterhow/eventpointpose"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/receding-moving-object-segmentation-in-3d","slug":"receding-moving-object-segmentation-in-3d","title":"Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions","date":"2022-06-08","arxiv_id":"2206.04129","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-learning-of-3d-scene-flow-from","slug":"unsupervised-learning-of-3d-scene-flow-from","title":"Unsupervised Learning of 3D Scene Flow from Monocular Camera","date":"2022-06-08","arxiv_id":"2206.03673","repositories_listed":1,"syntology":null},{"url":"/paper/hardware-accelerated-mars-sample-localization","slug":"hardware-accelerated-mars-sample-localization","title":"Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations","date":"2022-06-06","arxiv_id":"2206.02622","repositories_listed":1,"syntology":null},{"url":"/paper/mesh-based-dynamics-with-occlusion-reasoning","slug":"mesh-based-dynamics-with-occlusion-reasoning","title":"Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulation","date":"2022-06-06","arxiv_id":"2206.02881","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-the-role-of-image-retrieval-for","slug":"investigating-the-role-of-image-retrieval-for","title":"Investigating the Role of Image Retrieval for Visual Localization -- An exhaustive benchmark","date":"2022-05-31","arxiv_id":"2205.15761","repositories_listed":1,"syntology":null},{"url":"/paper/mask2hand-learning-to-predict-the-3d-hand","slug":"mask2hand-learning-to-predict-the-3d-hand","title":"Mask2Hand: Learning to Predict the 3D Hand Pose and Shape from Shadow","date":"2022-05-31","arxiv_id":"2205.15553","repositories_listed":1,"syntology":null},{"url":"/paper/samurai-shape-and-material-from-unconstrained","slug":"samurai-shape-and-material-from-unconstrained","title":"SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections","date":"2022-05-31","arxiv_id":"2205.15768","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/samurai-shape-and-material-from-unconstrained#ran","syntology_url":"https://syntology.ai/paper/2205.15768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.15768"}},"official":null}},{"url":"/paper/heater-an-efficient-and-unified-network-for","slug":"heater-an-efficient-and-unified-network-for","title":"FeatER: An Efficient Network for Human Reconstruction via Feature Map-Based TransformER","date":"2022-05-30","arxiv_id":"2205.15448","repositories_listed":1,"syntology":null},{"url":"/paper/voge-a-differentiable-volume-renderer-using","slug":"voge-a-differentiable-volume-renderer-using","title":"VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis","date":"2022-05-30","arxiv_id":"2205.15401","repositories_listed":1,"syntology":null},{"url":"/paper/revealing-the-dark-secrets-of-masked-image","slug":"revealing-the-dark-secrets-of-masked-image","title":"Revealing the Dark Secrets of Masked Image Modeling","date":"2022-05-26","arxiv_id":"2205.13543","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/revealing-the-dark-secrets-of-masked-image#ran","syntology_url":"https://syntology.ai/paper/2205.13543","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13543"}},"official":{"repos":["SwinTransformer/MIM-Depth-Estimation"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/onepose-one-shot-object-pose-estimation","slug":"onepose-one-shot-object-pose-estimation","title":"OnePose: One-Shot Object Pose Estimation without CAD Models","date":"2022-05-24","arxiv_id":"2205.12257","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/onepose-one-shot-object-pose-estimation#ran","syntology_url":"https://syntology.ai/paper/2205.12257","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.12257"}},"official":{"repos":["zju3dv/OnePose"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/keypoint-based-category-level-object-pose","slug":"keypoint-based-category-level-object-pose","title":"Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimation","date":"2022-05-23","arxiv_id":"2205.11047","repositories_listed":1,"syntology":null},{"url":"/paper/autolink-self-supervised-learning-of-human","slug":"autolink-self-supervised-learning-of-human","title":"AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints","date":"2022-05-21","arxiv_id":"2205.10636","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":4,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/autolink-self-supervised-learning-of-human#ran","syntology_url":"https://syntology.ai/paper/2205.10636","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10636"}},"official":{"repos":["xingzhehe/AutoLink-Self-supervised-Learning-of-Human-Skeletons-and-Object-Outlines-by-Linking-Keypoints"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fvor-robust-joint-shape-and-pose-optimization","slug":"fvor-robust-joint-shape-and-pose-optimization","title":"FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction","date":"2022-05-16","arxiv_id":"2205.07763","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-deep-visual-and-inertial-odometry","slug":"efficient-deep-visual-and-inertial-odometry","title":"Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection","date":"2022-05-12","arxiv_id":"2205.06187","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":3,"n_ran_checked":3,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":8,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/efficient-deep-visual-and-inertial-odometry#ran","syntology_url":"https://syntology.ai/paper/2205.06187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.06187"}},"official":{"repos":["mingyuyng/visual-selective-vio"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/aggpose-deep-aggregation-vision-transformer","slug":"aggpose-deep-aggregation-vision-transformer","title":"AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation","date":"2022-05-11","arxiv_id":"2205.05277","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":5,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"5 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 5 samples that ran constructed an object rather than computing a result","sample_list":"/paper/aggpose-deep-aggregation-vision-transformer#ran","syntology_url":"https://syntology.ai/paper/2205.05277","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.05277"}},"official":{"repos":["szar-lab/aggpose"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/bico-net-regress-globally-match-locally-for","slug":"bico-net-regress-globally-match-locally-for","title":"BiCo-Net: Regress Globally, Match Locally for Robust 6D Pose Estimation","date":"2022-05-07","arxiv_id":"2205.03536","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/bico-net-regress-globally-match-locally-for#ran","syntology_url":"https://syntology.ai/paper/2205.03536","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.03536"}},"official":{"repos":["gorilla-lab-scut/bico-net"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/mobile-ursonet-an-embeddable-neural-network","slug":"mobile-ursonet-an-embeddable-neural-network","title":"Mobile-URSONet: an Embeddable Neural Network for Onboard Spacecraft Pose Estimation","date":"2022-05-04","arxiv_id":"2205.02065","repositories_listed":1,"syntology":null},{"url":"/paper/lite-pose-efficient-architecture-design-for","slug":"lite-pose-efficient-architecture-design-for","title":"Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation","date":"2022-05-03","arxiv_id":"2205.01271","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/lite-pose-efficient-architecture-design-for#ran","syntology_url":"https://syntology.ai/paper/2205.01271","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.01271"}},"official":{"repos":["mit-han-lab/litepose"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-framework-for-real-time-fetal","slug":"deep-learning-framework-for-real-time-fetal","title":"Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI","date":"2022-05-02","arxiv_id":"2205.01675","repositories_listed":1,"syntology":null},{"url":"/paper/dual-networks-based-3d-multi-person-pose","slug":"dual-networks-based-3d-multi-person-pose","title":"Dual networks based 3D Multi-Person Pose Estimation from Monocular Video","date":"2022-05-02","arxiv_id":"2205.00748","repositories_listed":1,"syntology":null},{"url":"/paper/streaming-multiscale-deep-equilibrium-models","slug":"streaming-multiscale-deep-equilibrium-models","title":"Representation Recycling for Streaming Video Analysis","date":"2022-04-28","arxiv_id":"2204.13492","repositories_listed":1,"syntology":null},{"url":"/paper/context-aware-sequence-alignment-using-4d","slug":"context-aware-sequence-alignment-using-4d","title":"Context-Aware Sequence Alignment using 4D Skeletal Augmentation","date":"2022-04-26","arxiv_id":"2204.12223","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/context-aware-sequence-alignment-using-4d#ran","syntology_url":"https://syntology.ai/paper/2204.12223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.12223"}},"official":null}},{"url":"/paper/coupled-iterative-refinement-for-6d-multi","slug":"coupled-iterative-refinement-for-6d-multi","title":"Coupled Iterative Refinement for 6D Multi-Object Pose Estimation","date":"2022-04-26","arxiv_id":"2204.12516","repositories_listed":1,"syntology":null},{"url":"/paper/pedrecnet-multi-task-deep-neural-network-for","slug":"pedrecnet-multi-task-deep-neural-network-for","title":"PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation","date":"2022-04-25","arxiv_id":"2204.11548","repositories_listed":1,"syntology":null},{"url":"/paper/dite-hrnet-dynamic-lightweight-high","slug":"dite-hrnet-dynamic-lightweight-high","title":"Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose Estimation","date":"2022-04-22","arxiv_id":"2204.10762","repositories_listed":1,"syntology":null},{"url":"/paper/gen6d-generalizable-model-free-6-dof-object","slug":"gen6d-generalizable-model-free-6-dof-object","title":"Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images","date":"2022-04-22","arxiv_id":"2204.10776","repositories_listed":1,"syntology":null},{"url":"/paper/not-all-tokens-are-equal-human-centric-visual","slug":"not-all-tokens-are-equal-human-centric-visual","title":"Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer","date":"2022-04-19","arxiv_id":"2204.08680","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/not-all-tokens-are-equal-human-centric-visual#ran","syntology_url":"https://syntology.ai/paper/2204.08680","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.08680"}},"official":{"repos":["zengwang430521/tcformer"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-equivariant-learning-for","slug":"self-supervised-equivariant-learning-for","title":"Self-Supervised Equivariant Learning for Oriented Keypoint Detection","date":"2022-04-19","arxiv_id":"2204.08613","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-supervised-equivariant-learning-for#ran","syntology_url":"https://syntology.ai/paper/2204.08613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.08613"}},"official":{"repos":["bluedream1121/REKD"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/what-s-in-your-hands-3d-reconstruction-of","slug":"what-s-in-your-hands-3d-reconstruction-of","title":"What's in your hands? 3D Reconstruction of Generic Objects in Hands","date":"2022-04-14","arxiv_id":"2204.07153","repositories_listed":1,"syntology":null},{"url":"/paper/reuse-your-features-unifying-retrieval-and","slug":"reuse-your-features-unifying-retrieval-and","title":"Reuse your features: unifying retrieval and feature-metric alignment","date":"2022-04-13","arxiv_id":"2204.06292","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-keypoint-based-pose-estimation-from","slug":"semantic-keypoint-based-pose-estimation-from","title":"Semantic keypoint-based pose estimation from single RGB frames","date":"2022-04-12","arxiv_id":"2204.05864","repositories_listed":1,"syntology":null},{"url":"/paper/bimodal-camera-pose-prediction-for-endoscopy","slug":"bimodal-camera-pose-prediction-for-endoscopy","title":"Bimodal Camera Pose Prediction for Endoscopy","date":"2022-04-11","arxiv_id":"2204.04968","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-cross-view-image-retrieval-highly","slug":"beyond-cross-view-image-retrieval-highly","title":"Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image","date":"2022-04-10","arxiv_id":"2204.04752","repositories_listed":1,"syntology":null},{"url":"/paper/dad-3dheads-a-large-scale-dense-accurate-and","slug":"dad-3dheads-a-large-scale-dense-accurate-and","title":"DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image","date":"2022-04-07","arxiv_id":"2204.03688","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-category-level-object-pose","slug":"zero-shot-category-level-object-pose","title":"Zero-Shot Category-Level Object Pose Estimation","date":"2022-04-07","arxiv_id":"2204.03635","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-framework-for-domain-adaptive-pose","slug":"a-unified-framework-for-domain-adaptive-pose","title":"A Unified Framework for Domain Adaptive Pose Estimation","date":"2022-04-01","arxiv_id":"2204.00172","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-unified-framework-for-domain-adaptive-pose#ran","syntology_url":"https://syntology.ai/paper/2204.00172","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00172"}},"official":{"repos":["visionlearninggroup/uda_poseestimation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dfnet-enhance-aboslute-pose-regression-with","slug":"dfnet-enhance-aboslute-pose-regression-with","title":"DFNet: Enhance Absolute Pose Regression with Direct Feature Matching","date":"2022-04-01","arxiv_id":"2204.00559","repositories_listed":1,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":13,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dfnet-enhance-aboslute-pose-regression-with#ran","syntology_url":"https://syntology.ai/paper/2204.00559","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00559"}},"official":{"repos":["activevisionlab/dfnet"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unimodal-concentrated-loss-fully-adaptive","slug":"unimodal-concentrated-loss-fully-adaptive","title":"Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression","date":"2022-04-01","arxiv_id":"2204.00309","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unimodal-concentrated-loss-fully-adaptive#ran","syntology_url":"https://syntology.ai/paper/2204.00309","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.00309"}},"official":null}},{"url":"/paper/balanced-mse-for-imbalanced-visual-regression","slug":"balanced-mse-for-imbalanced-visual-regression","title":"Balanced MSE for Imbalanced Visual Regression","date":"2022-03-30","arxiv_id":"2203.16427","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/balanced-mse-for-imbalanced-visual-regression#ran","syntology_url":"https://syntology.ai/paper/2203.16427","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16427"}},"official":{"repos":["jiawei-ren/BalancedMSE"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/efficient-virtual-view-selection-for-3d-hand","slug":"efficient-virtual-view-selection-for-3d-hand","title":"Efficient Virtual View Selection for 3D Hand Pose Estimation","date":"2022-03-29","arxiv_id":"2203.15458","repositories_listed":1,"syntology":null},{"url":"/paper/oakink-a-large-scale-knowledge-repository-for","slug":"oakink-a-large-scale-knowledge-repository-for","title":"OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction","date":"2022-03-29","arxiv_id":"2203.15709","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/oakink-a-large-scale-knowledge-repository-for#ran","syntology_url":"https://syntology.ai/paper/2203.15709","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15709"}},"official":{"repos":["lixiny/oakink"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/posetriplet-co-evolving-3d-human-pose","slug":"posetriplet-co-evolving-3d-human-pose","title":"PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision","date":"2022-03-29","arxiv_id":"2203.15625","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-feature-alignment-and-mutual","slug":"temporal-feature-alignment-and-mutual","title":"Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation","date":"2022-03-29","arxiv_id":"2203.15227","repositories_listed":1,"syntology":null},{"url":"/paper/fs6d-few-shot-6d-pose-estimation-of-novel","slug":"fs6d-few-shot-6d-pose-estimation-of-novel","title":"FS6D: Few-Shot 6D Pose Estimation of Novel Objects","date":"2022-03-28","arxiv_id":"2203.14628","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-elimination-templates-by-greedy","slug":"optimizing-elimination-templates-by-greedy","title":"Optimizing Elimination Templates by Greedy Parameter Search","date":"2022-03-28","arxiv_id":"2203.14901","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/optimizing-elimination-templates-by-greedy#ran","syntology_url":"https://syntology.ai/paper/2203.14901","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14901"}},"official":{"repos":["martyushev/eliminationtemplates"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/regtr-end-to-end-point-cloud-correspondences","slug":"regtr-end-to-end-point-cloud-correspondences","title":"REGTR: End-to-end Point Cloud Correspondences with Transformers","date":"2022-03-28","arxiv_id":"2203.14517","repositories_listed":1,"syntology":{"n":16,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/regtr-end-to-end-point-cloud-correspondences#ran","syntology_url":"https://syntology.ai/paper/2203.14517","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14517"}},"official":{"repos":["yewzijian/regtr"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/a-visual-navigation-perspective-for-category","slug":"a-visual-navigation-perspective-for-category","title":"A Visual Navigation Perspective for Category-Level Object Pose Estimation","date":"2022-03-25","arxiv_id":"2203.13572","repositories_listed":1,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 2 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/a-visual-navigation-perspective-for-category#ran","syntology_url":"https://syntology.ai/paper/2203.13572","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.13572"}},"official":{"repos":["wrld/visual_navigation_pose_estimation"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/crossformer-cross-spatio-temporal-transformer","slug":"crossformer-cross-spatio-temporal-transformer","title":"CrossFormer: Cross Spatio-Temporal Transformer for 3D Human Pose Estimation","date":"2022-03-24","arxiv_id":"2203.13387","repositories_listed":1,"syntology":null},{"url":"/paper/epro-pnp-generalized-end-to-end-probabilistic","slug":"epro-pnp-generalized-end-to-end-probabilistic","title":"EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation","date":"2022-03-24","arxiv_id":"2203.13254","repositories_listed":1,"syntology":null},{"url":"/paper/rnnpose-recurrent-6-dof-object-pose","slug":"rnnpose-recurrent-6-dof-object-pose","title":"RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization","date":"2022-03-24","arxiv_id":"2203.12870","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/rnnpose-recurrent-6-dof-object-pose#ran","syntology_url":"https://syntology.ai/paper/2203.12870","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12870"}},"official":{"repos":["decayale/rnnpose"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/ray3d-ray-based-3d-human-pose-estimation-for","slug":"ray3d-ray-based-3d-human-pose-estimation-for","title":"Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization","date":"2022-03-22","arxiv_id":"2203.11471","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/ray3d-ray-based-3d-human-pose-estimation-for#ran","syntology_url":"https://syntology.ai/paper/2203.11471","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11471"}},"official":{"repos":["YxZhxn/Ray3D"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/occlusion-aware-self-supervised-monocular-6d","slug":"occlusion-aware-self-supervised-monocular-6d","title":"Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation","date":"2022-03-19","arxiv_id":"2203.10339","repositories_listed":1,"syntology":null},{"url":"/paper/perspective-flow-aggregation-for-data-limited","slug":"perspective-flow-aggregation-for-data-limited","title":"Perspective Flow Aggregation for Data-Limited 6D Object Pose Estimation","date":"2022-03-18","arxiv_id":"2203.09836","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/perspective-flow-aggregation-for-data-limited#ran","syntology_url":"https://syntology.ai/paper/2203.09836","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09836"}},"official":{"repos":["cvlab-epfl/perspective-flow-aggregation"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/viewformer-nerf-free-neural-rendering-from","slug":"viewformer-nerf-free-neural-rendering-from","title":"ViewFormer: NeRF-free Neural Rendering from Few Images Using Transformers","date":"2022-03-18","arxiv_id":"2203.10157","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/viewformer-nerf-free-neural-rendering-from#ran","syntology_url":"https://syntology.ai/paper/2203.10157","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10157"}},"official":{"repos":["jkulhanek/viewformer"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/matchformer-interleaving-attention-in","slug":"matchformer-interleaving-attention-in","title":"MatchFormer: Interleaving Attention in Transformers for Feature Matching","date":"2022-03-17","arxiv_id":"2203.09645","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/matchformer-interleaving-attention-in#ran","syntology_url":"https://syntology.ai/paper/2203.09645","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09645"}},"official":{"repos":["jamycheung/matchformer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/zebrapose-coarse-to-fine-surface-encoding-for","slug":"zebrapose-coarse-to-fine-surface-encoding-for","title":"ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation","date":"2022-03-17","arxiv_id":"2203.09418","repositories_listed":1,"syntology":{"n":15,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/zebrapose-coarse-to-fine-surface-encoding-for#ran","syntology_url":"https://syntology.ai/paper/2203.09418","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09418"}},"official":{"repos":["suyz526/zebrapose"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/deciwatch-a-simple-baseline-for-10x-efficient","slug":"deciwatch-a-simple-baseline-for-10x-efficient","title":"DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation","date":"2022-03-16","arxiv_id":"2203.08713","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deciwatch-a-simple-baseline-for-10x-efficient#ran","syntology_url":"https://syntology.ai/paper/2203.08713","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08713"}},"official":{"repos":["cure-lab/DeciWatch"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-sensitivity-of-pose-estimation-neural","slug":"on-the-sensitivity-of-pose-estimation-neural","title":"On the sensitivity of pose estimation neural networks: rotation parameterizations, Lipschitz constants, and provable bounds","date":"2022-03-16","arxiv_id":"2203.09937","repositories_listed":1,"syntology":null},{"url":"/paper/posepipe-open-source-human-pose-estimation","slug":"posepipe-open-source-human-pose-estimation","title":"PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Research","date":"2022-03-16","arxiv_id":"2203.08792","repositories_listed":1,"syntology":null},{"url":"/paper/distribution-aware-single-stage-models-for","slug":"distribution-aware-single-stage-models-for","title":"Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation","date":"2022-03-15","arxiv_id":"2203.07697","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/distribution-aware-single-stage-models-for#ran","syntology_url":"https://syntology.ai/paper/2203.07697","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07697"}},"official":null}},{"url":"/paper/p-stmo-pre-trained-spatial-temporal-many-to","slug":"p-stmo-pre-trained-spatial-temporal-many-to","title":"P-STMO: Pre-Trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimation","date":"2022-03-15","arxiv_id":"2203.07628","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":6,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"9 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/p-stmo-pre-trained-spatial-temporal-many-to#ran","syntology_url":"https://syntology.ai/paper/2203.07628","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07628"}},"official":{"repos":["patrick-swk/p-stmo"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":6,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/6-dof-pose-estimation-of-household-objects","slug":"6-dof-pose-estimation-of-household-objects","title":"6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark","date":"2022-03-11","arxiv_id":"2203.05701","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/6-dof-pose-estimation-of-household-objects#ran","syntology_url":"https://syntology.ai/paper/2203.05701","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05701"}},"official":{"repos":["swtyree/hope-dataset"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/clearpose-large-scale-transparent-object","slug":"clearpose-large-scale-transparent-object","title":"ClearPose: Large-scale Transparent Object Dataset and Benchmark","date":"2022-03-08","arxiv_id":"2203.03890","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/clearpose-large-scale-transparent-object#ran","syntology_url":"https://syntology.ai/paper/2203.03890","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.03890"}},"official":{"repos":["opipari/clearpose"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/quantification-of-occlusion-handling","slug":"quantification-of-occlusion-handling","title":"Quantification of Occlusion Handling Capability of a 3D Human Pose Estimation Framework","date":"2022-03-08","arxiv_id":"2203.04113","repositories_listed":1,"syntology":null},{"url":"/paper/robust-multi-task-learning-and-online","slug":"robust-multi-task-learning-and-online","title":"Robust Multi-Task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gap","date":"2022-03-08","arxiv_id":"2203.04275","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/robust-multi-task-learning-and-online#ran","syntology_url":"https://syntology.ai/paper/2203.04275","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.04275"}},"official":{"repos":["tpark94/spnv2"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/cppf-towards-robust-category-level-9d-pose","slug":"cppf-towards-robust-category-level-9d-pose","title":"CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild","date":"2022-03-07","arxiv_id":"2203.03089","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cppf-towards-robust-category-level-9d-pose#ran","syntology_url":"https://syntology.ai/paper/2203.03089","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.03089"}},"official":{"repos":["qq456cvb/cppf"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mixste-seq2seq-mixed-spatio-temporal-encoder","slug":"mixste-seq2seq-mixed-spatio-temporal-encoder","title":"MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video","date":"2022-03-02","arxiv_id":"2203.00859","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/mixste-seq2seq-mixed-spatio-temporal-encoder#ran","syntology_url":"https://syntology.ai/paper/2203.00859","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.00859"}},"official":{"repos":["JinluZhang1126/MixSTE"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/object-pose-estimation-using-mid-level-visual","slug":"object-pose-estimation-using-mid-level-visual","title":"Object Pose Estimation using Mid-level Visual Representations","date":"2022-03-02","arxiv_id":"2203.01449","repositories_listed":1,"syntology":null}],"record_sha256":"730cc6f9f6e236e37b281a0cb52cd778bbb21b5bc5ee8227bd1fbddb9a5c1a5f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}