Summary of Distribution and Depth-aware Transformers For 3d Human Mesh Recovery, by Jerrin Bright et al.
Distribution and Depth-Aware Transformers for 3D Human Mesh Recovery
by Jerrin Bright, Bavesh Balaji, Harish Prakash, Yuhao Chen, David A Clausi, John Zelek
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of recovering human meshes from single images, a problem often hindered by depth ambiguities and reduced precision. Existing approaches resort to pose priors or multi-modal data, neglecting the valuable scene-depth information in a single image. To overcome these limitations, the authors introduce Distribution and depth-aware human mesh recovery (D2A-HMR), an end-to-end transformer architecture that incorporates scene-depth leveraging prior depth information. This approach demonstrates superior performance handling out-of-distribution data in certain scenarios while achieving competitive results against state-of-the-art methods on controlled datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make it easier for computers to understand how people look from just one picture. Right now, this is hard because the computer might not know which parts of the person are closer or farther away. To fix this, the researchers created a new way to do this called D2A-HMR, which uses special computer code that helps the computer figure out what’s going on in the picture. |
Keywords
» Artificial intelligence » Multi modal » Precision » Transformer
