Enhancing Hands in 3D Whole-Body Pose Estimation with Conditional Hands Modulator
Accurately recovering hand poses within the body context remains a major challenge in 3D whole-body pose estimation. This difficulty arises from a fundamental supervision gap: whole-body pose estimators are trained on full-body datasets with limited hand diversity, while hand-only estimators, trained on hand-centric datasets, excel at detailed finger articulation but lack global body awareness. To address this, we propose Hand4Whole++, a modular framework that leverages the strengths of both pre-trained whole-body and hand pose estimators. We introduce CHAM (Conditional Hands Modulator), a lightweight module that modulates the whole-body feature stream using hand-specific features extract...
| Authors | Gyeongsik Moon |
|---|---|
| Primary category | cs.CV |
| Published | 16 March 2026 |
| Last updated at source | 16 March 2026 |
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