Summary of Hands3c: 3d Hand Mesh Reconstruction with State Space Spatial Channel Attention From Rgb Images, by Zixun Jiao et al.
HandS3C: 3D Hand Mesh Reconstruction with State Space Spatial Channel Attention from RGB images
by Zixun Jiao, Xihan Wang, Zhaoqiang Xia, Lianhe Shao, Quanli Gao
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 proposes a novel 3D hand mesh reconstruction network, called HandS3C, which uses a state-space model to achieve high-performance 3D hand mesh reconstruction with efficient computation. The network incorporates a spatial-channel attention module that extends the receptive field and extracts hand features in the spatial dimension, enabling the reconstruction of complete and detailed hand meshes. Experiments on datasets with heavy occlusions (FREIHAND, DEXYCB, and HO3D) demonstrate state-of-the-art performance while maintaining minimal parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create better computer vision systems that can understand human hands. It’s like having a superpower that lets computers see and learn from our hands in 3D. The problem is that it’s hard to get accurate pictures of hands because they often get hidden by other things. To solve this, the researchers created a new way to process images using something called state-space models. This method is really good at recognizing patterns in hand movements and shapes. It also helps computers focus on the most important parts of an image, like the shape of someone’s fingers. The results are amazing – the computer can accurately recreate a 3D model of a hand from just one picture! |
Keywords
» Artificial intelligence » Attention
