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About this paper
Sheng Jin, Lumin Xu, Jin Xu, Can Wang, Wentao Liu, Chen Qian, Wanli Ouyang, Ping Luo
Researchers developed COCO-WholeBody, a large-scale dataset with dense whole-body keypoint annotations, and ZoomNet, a single multi-resolution network for integrated whole-body pose estimation. ZoomNet achieved a whole-body AP of 0.541 on the new dataset, outperforming previous fragmented methods.
Key insights
- Holistic whole-body pose estimation requires addressing the extreme scale variations between different body parts like the face and hands.
- A multi-resolution approach with 'zoom-in' capabilities is effective for precisely detecting fine-grained keypoints in smaller, detail-rich regions.
- A large-scale, densely annotated dataset encompassing the entire human body is fundamental for developing and evaluating unified whole-body pose estimation models.
Summary by alphaXiv.
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