{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/code/build-rotation","entry":"build_rotation","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":8,"n_papers_ran":7,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":6,"n_samples_ran":5,"n_samples_fingerprinted":2,"n_places":8,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":0,"ran":2,"unverified":1},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2508.04929","paper":"/paper/arxiv-2508-04929","title":"CryoSplat: Gaussian Splatting for Cryo-EM Homogeneous Reconstruction","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"Chen-Suyi/cryosplat","path":"gaussian_model.py","file_url":"https://github.com/Chen-Suyi/cryosplat/blob/HEAD/gaussian_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"470f3cd8086ba7a2","mcp_get_code":{"code_sha256":"470f3cd8086ba7a2"}},{"arxiv_id":"2411.16443","paper":"/paper/splatflow-multi-view-rectified-flow-model-for","title":"SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis","date":"2024-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gohyojun15/SplatFlow","path":"model/refiner/gs_util.py","file_url":"https://github.com/gohyojun15/SplatFlow/blob/HEAD/model/refiner/gs_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4a20087245ac0d11","mcp_get_code":{"code_sha256":"4a20087245ac0d11"}},{"arxiv_id":"2406.17601","paper":"/paper/director3d-real-world-camera-trajectory-and","title":"Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text","date":"2024-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"imlixinyang/director3d","path":"modules/refiners/gs_utils.py","file_url":"https://github.com/imlixinyang/director3d/blob/HEAD/modules/refiners/gs_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"c34231087ef1ed71","mcp_get_code":{"code_sha256":"c34231087ef1ed71"}},{"arxiv_id":"2406.06050","paper":"/paper/generalizable-human-gaussians-from-single","title":"Generalizable Human Gaussians from Single-View Image","date":"2024-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jinnan-chen/HGM","path":"infer.py","file_url":"https://github.com/jinnan-chen/HGM/blob/HEAD/infer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f6b15a508c496955","mcp_get_code":{"code_sha256":"f6b15a508c496955"}},{"arxiv_id":"2406.02968","paper":"/paper/adversarial-generation-of-hierarchical","title":"GSGAN: Adversarial Learning for Hierarchical Generation of 3D Gaussian Splats","date":"2024-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hse1032/Adversarial-Generation-of-Hierarchical-Gaussians-for-3D-Generative-Model","path":"training/gaussian3d_splatting/gaussian_model.py","file_url":"https://github.com/hse1032/Adversarial-Generation-of-Hierarchical-Gaussians-for-3D-Generative-Model/blob/HEAD/training/gaussian3d_splatting/gaussian_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"470f3cd8086ba7a2","mcp_get_code":{"code_sha256":"470f3cd8086ba7a2"}},{"arxiv_id":"2404.03736","paper":"/paper/sc4d-sparse-controlled-video-to-4d-generation","title":"SC4D: Sparse-Controlled Video-to-4D Generation and Motion Transfer","date":"2024-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jarrentwu1031/sc4d","path":"helpers.py","file_url":"https://github.com/jarrentwu1031/sc4d/blob/HEAD/helpers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d46f0f7778585fae","mcp_get_code":{"code_sha256":"d46f0f7778585fae"}},{"arxiv_id":"2403.07494","paper":"/paper/semgauss-slam-dense-semantic-gaussian","title":"SemGauss-SLAM: Dense Semantic Gaussian Splatting SLAM","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IRMVLab/SemGauss-SLAM","path":"utils/slam_external.py","file_url":"https://github.com/IRMVLab/SemGauss-SLAM/blob/HEAD/utils/slam_external.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d46f0f7778585fae","mcp_get_code":{"code_sha256":"d46f0f7778585fae"}},{"arxiv_id":"2402.03246","paper":"/paper/sgs-slam-semantic-gaussian-splatting-for","title":"SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM","date":"2024-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shuhongll/sgs-slam","path":"utils/gs_helpers.py","file_url":"https://github.com/shuhongll/sgs-slam/blob/HEAD/utils/gs_helpers.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"20a77144ffa65865","mcp_get_code":{"code_sha256":"20a77144ffa65865"}}]}