Summary of Fine-grained Multi-view Hand Reconstruction Using Inverse Rendering, by Qijun Gan et al.
Fine-Grained Multi-View Hand Reconstruction Using Inverse Rendering
by Qijun Gan, Wentong Li, Jinwei Ren, Jianke Zhu
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a novel method for reconstructing high-fidelity hand models with intricate textures. The approach leverages inverse rendering to restore hand poses and details, which is particularly useful in enhancing human-object interaction and advancing real-world applications. The proposed method consists of two main components: firstly, it predicts a parametric hand mesh model through Graph Convolutional Networks (GCN) based on multi-view images; secondly, it introduces a novel Hand Albedo and Mesh (HAM) optimization module to refine both the hand mesh and textures. Furthermore, an effective mesh-based neural rendering scheme is suggested to simultaneously generate photo-realistic images and optimize mesh geometry by fusing pre-trained rendering networks with vertex features. Experimental results on InterHand2.6M, DeepHandMesh, and a self-collected dataset demonstrate that the proposed approach outperforms state-of-the-art methods in terms of reconstruction accuracy and rendering quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating super-realistic hand models for robots or computers to use. Right now, it’s hard to get these models right because they can be tricky to capture with cameras. The researchers came up with a new way to do this using special computer programs that learn from lots of pictures. They tested their method on three different datasets and showed that it works better than other methods in making accurate hand models. |
Keywords
» Artificial intelligence » Gcn » Optimization
