{"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":"/paper/adaptively-multi-view-and-temporal-fusing","title":"Adaptive Multi-view and Temporal Fusing Transformer for 3D Human Pose Estimation","arxiv_id":"2110.05092","date":"2021-10-11","proceeding":null,"authors":["Hui Shuai","Lele Wu","Qingshan Liu"],"abstract":"This paper proposes a unified framework dubbed Multi-view and Temporal Fusing Transformer (MTF-Transformer) to adaptively handle varying view numbers and video length without camera calibration in 3D Human Pose Estimation (HPE). It consists of Feature Extractor, Multi-view Fusing Transformer (MFT), and Temporal Fusing Transformer (TFT). Feature Extractor estimates 2D pose from each image and fuses the prediction according to the confidence. It provides pose-focused feature embedding and makes subsequent modules computationally lightweight. MFT fuses the features of a varying number of views with a novel Relative-Attention block. It adaptively measures the implicit relative relationship between each pair of views and reconstructs more informative features. TFT aggregates the features of the whole sequence and predicts 3D pose via a transformer. It adaptively deals with the video of arbitrary length and fully unitizes the temporal information. The migration of transformers enables our model to learn spatial geometry better and preserve robustness for varying application scenarios. We report quantitative and qualitative results on the Human3.6M, TotalCapture, and KTH Multiview Football II. Compared with state-of-the-art methods with camera parameters, MTF-Transformer obtains competitive results and generalizes well to dynamic capture with an arbitrary number of unseen views.","url_abs":"https://arxiv.org/abs/2110.05092v2","url_pdf":"https://arxiv.org/pdf/2110.05092v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"camera-calibration","task_name":"Camera Calibration"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-human-pose-estimation-on-human36m","task":"3D Human Pose Estimation","dataset":"Human3.6M","model":"MTF-Transformer (M=0.4, T=7)","rank_in_archive_order":4,"of":88,"metrics":{"Average MPJPE (mm)":"28.5","Multi-View or Monocular":"Multi-View","Using 2D ground-truth joints":"No"},"uses_additional_data":false},{"leaderboard":"/sota/3d-human-pose-estimation-on-human36m","task":"3D Human Pose Estimation","dataset":"Human3.6M","model":"MTF-Transformer (M=0.4, T=1)","rank_in_archive_order":6,"of":88,"metrics":{"Average MPJPE (mm)":"29.4","Multi-View or Monocular":"Multi-View","Using 2D ground-truth joints":"No"},"uses_additional_data":false},{"leaderboard":"/sota/3d-human-pose-estimation-on-human36m","task":"3D Human Pose Estimation","dataset":"Human3.6M","model":"MTF-Transformer (M=0.4, T=7, N=1)","rank_in_archive_order":59,"of":88,"metrics":{"Average MPJPE (mm)":"49.4","Multi-View or Monocular":"Monocular","Using 2D ground-truth joints":"No"},"uses_additional_data":false},{"leaderboard":"/sota/3d-human-pose-estimation-on-human36m","task":"3D Human Pose Estimation","dataset":"Human3.6M","model":"MTF-Transformer (M=0.4, T=1, N=1)","rank_in_archive_order":68,"of":88,"metrics":{"Average MPJPE (mm)":"50.7","Multi-View or Monocular":"Monocular","Using 2D ground-truth joints":"No"},"uses_additional_data":false},{"leaderboard":"/sota/3d-human-pose-estimation-on-total-capture","task":"3D Human Pose Estimation","dataset":"Total Capture","model":"MTF-Transformer (M=0.4, T=7)","rank_in_archive_order":7,"of":14,"metrics":{"Average MPJPE (mm)":"29.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2110.05092","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}