SAM 3D Body: Robust Full-Body Human Mesh Recovery
This work addresses the challenge of robust 3D human pose and shape estimation from images for applications in computer vision and graphics, representing a strong specific gain rather than a foundational advancement.
The paper tackles the problem of single-image full-body 3D human mesh recovery by introducing SAM 3D Body, which achieves state-of-the-art performance with strong generalization and consistent accuracy in diverse in-the-wild conditions, as shown by superior generalization and substantial improvements in user preference studies and quantitative analysis.
We introduce SAM 3D Body (3DB), a promptable model for single-image full-body 3D human mesh recovery (HMR) that demonstrates state-of-the-art performance, with strong generalization and consistent accuracy in diverse in-the-wild conditions. 3DB estimates the human pose of the body, feet, and hands. It is the first model to use a new parametric mesh representation, Momentum Human Rig (MHR), which decouples skeletal structure and surface shape. 3DB employs an encoder-decoder architecture and supports auxiliary prompts, including 2D keypoints and masks, enabling user-guided inference similar to the SAM family of models. We derive high-quality annotations from a multi-stage annotation pipeline that uses various combinations of manual keypoint annotation, differentiable optimization, multi-view geometry, and dense keypoint detection. Our data engine efficiently selects and processes data to ensure data diversity, collecting unusual poses and rare imaging conditions. We present a new evaluation dataset organized by pose and appearance categories, enabling nuanced analysis of model behavior. Our experiments demonstrate superior generalization and substantial improvements over prior methods in both qualitative user preference studies and traditional quantitative analysis. Both 3DB and MHR are open-source.