Patch-based 3D Human Pose Refinement
Abstract
State-of-the-art 3D human pose estimation approaches typically estimate pose from the entire RGB image in a single forward run. In this paper, we develop a post-processing step to refine 3D human pose estimation from body part patches. Using local patches as input has two advantages. First, the fine details around body parts are zoomed in to high resolution for preciser 3D pose prediction. Second, it enables the part appearance to be shared between poses to benefit rare poses. In order to acquire informative representation of patches, we explore different input modalities and validate the superiority of fusing predicted segmentation with RGB. We show that our method consistently boosts the accuracy of state-of-the-art 3D human pose methods.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 16, 2019
- Accession Number
- AD1154474
Entities
People
- Alan L. Yuille
- Qingfu Wan
- Weichao Qiu
Organizations
- Fudan University
- Johns Hopkins University