Summary of Maskhand: Generative Masked Modeling For Robust Hand Mesh Reconstruction in the Wild, by Muhammad Usama Saleem et al.
MaskHand: Generative Masked Modeling for Robust Hand Mesh Reconstruction in the Wild
by Muhammad Usama Saleem, Ekkasit Pinyoanuntapong, Mayur Jagdishbhai Patel, Hongfei Xue, Ahmed Helmy, Srijan Das, Pu Wang
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed MaskHand model is a generative masked model for 3D hand mesh recovery, addressing the challenges of complex articulations, self-occlusions, and depth ambiguities in traditional discriminative methods. By learning and sampling from the probabilistic distribution of the ambiguous 2D-to-3D mapping process, MaskHand synthesizes plausible 3D hand meshes with low uncertainty and high precision. The model consists of two key components: VQ-MANO, which encodes 3D hand articulations as discrete pose tokens in a latent space, and Context-Guided Masked Transformer that learns the joint distribution of masked pose tokens conditioned on corrupted token sequence, image context, and 2D pose cues. Extensive evaluations demonstrate state-of-the-art accuracy, robustness, and realism in 3D hand mesh reconstruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MaskHand is a new way to create 3D models of hands from single-color images. This is important because it’s hard to get accurate 3D models just from looking at a flat image. Most methods try to find the correct 3D model by learning a direct mapping from the 2D image to the 3D mesh, but this can be tricky because there are many possible answers. MaskHand takes a different approach by generating plausible 3D hand meshes by learning and sampling from the probabilistic distribution of the ambiguous 2D-to-3D mapping process. |
Keywords
» Artificial intelligence » Latent space » Precision » Token » Transformer
