A Twist Representation and Shape Refinement Method for Human Mesh Recovery
Bibliographic record
Abstract
3D human mesh recovery from single RGB images or monocular videos is a challenging task. The twist representation utilized in existing inverse kinematics-based methods fails to accurately describe the twisting posture when the estimated bone direction is imprecise. Additionally, supervising SMPL shape parameters has the issue of shape estimation overfitting due to limited training data. This often results in compromised bone lengths that subsequently impair the precision of joint positions. To address these issues, we propose a framework that breaks down both human pose and shape into finer components, effectively managing and minimizing errors within each component. The proposed framework integrates two key advancements: the advanced Ortho-Twist and Swing Representation (OTSR) and the Skeleton-Focused Shape Refinement (SFSR). OTSR offers a more sophisticated representation for limb rotations compared to the traditional twist angle and swing representation to enhance the accuracy of twisting posture estimation. SFSR refines the estimated SMPL shape parameters by fitting bone lengths using the estimated joint positions, thereby significantly mitigating shape overfitting and enhancing joint position accuracy in the recovered mesh. We conduct experiments on the Human3.6 M and 3DPW datasets. The results demonstrate the superiority of the proposed framework in both single-image and video scenarios. Additionally, the ablation studies confirm the effectiveness of our proposed modules, and further generalizability experiments demonstrate that our two key advancements can serve as plug-and-play modules to enhance existing methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".