Papers › ARTS: Semi-Analytical Regressor using Disentangled Skeletal Representations for Human...
ARTS: Semi-Analytical Regressor using Disentangled Skeletal Representations for Human Mesh Recovery from Videos
Tao Tang, Hong Liu, Yingxuan You, Ti Wang, Wenhao Li
Although existing video-based 3D human mesh recovery methods have made significant progress, simultaneously estimating human pose and shape from low-resolution image features limits their performance. These image features lack sufficient spatial information about the human body and contain various noises (e.g., background, lighting, and clothing), which often results in inaccurate pose and inconsistent motion. Inspired by the rapid advance in human pose estimation, we discover that compared to image features, skeletons inherently contain accurate human pose and motion. Therefore, we propose a novel semiAnalytical Regressor using disenTangled Skeletal representations for human mesh recovery from videos, called ARTS. Specifically, a skeleton estimation and disentanglement module is proposed to estimate the 3D skeletons from a video and decouple them into disentangled skeletal representations (i.e., joint position, bone length, and human motion). Then, to fully utilize these representations, we introduce a semi-analytical regressor to estimate the parameters of the human mesh model. The regressor consists of three modules: Temporal Inverse Kinematics (TIK), Bone-guided Shape Fitting (BSF), and Motion-Centric Refinement (MCR). TIK utilizes joint position to estimate initial pose parameters and BSF leverages bone length to regress bone-aligned shape parameters. Finally, MCR combines human motion representation with image features to refine the initial human model parameters. Extensive experiments demonstrate that our ARTS surpasses existing state-of-the-art video-based methods in both per-frame accuracy and temporal consistency on popular benchmarks: 3DPW, MPI-INF-3DHP, and Human3.6M. Code is available at https://github.com/TangTao-PKU/ARTS.
Code
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Human Pose Estimation | 3DPW | ARTS (Resnet50 L=16) | Acceleration Error | 6.5 | #19 of 119 | Archive leaderboard | report |
| 3D Human Pose Estimation | 3DPW | ARTS (Resnet50 L=16) | MPJPE | 67.7 | #19 of 119 | Archive leaderboard | report |
| 3D Human Pose Estimation | 3DPW | ARTS (Resnet50 L=16) | MPVPE | 81.4 | #19 of 119 | Archive leaderboard | report |
| 3D Human Pose Estimation | 3DPW | ARTS (Resnet50 L=16) | PA-MPJPE | 46.5 | #19 of 119 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | ARTS (Resnet50 L=16) | Acceleration Error | 7.4 | #30 of 108 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | ARTS (Resnet50 L=16) | MPJPE | 71.8 | #30 of 108 | Archive leaderboard | report |
| 3D Human Pose Estimation | MPI-INF-3DHP | ARTS (Resnet50 L=16) | PA-MPJPE | 53 | #30 of 108 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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