Summary of Representing Animatable Avatar Via Factorized Neural Fields, by Chunjin Song et al.
Representing Animatable Avatar via Factorized Neural Fields
by Chunjin Song, Zhijie Wu, Bastian Wandt, Leonid Sigal, Helge Rhodin
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 paper presents a novel approach for reconstructing high-fidelity human 3D models from monocular videos. The proposed method, dubbed a dual-branch network, factorizes the per-frame rendering results into pose-independent and pose-dependent components to facilitate frame consistency. This allows for the preservation of both coarse body contours and fine-grained texture features that are time-variant. The model integrates information predicted by two branches, utilizing volume rendering to generate photo-realistic 3D human images. Experimental results demonstrate that the proposed method surpasses state-of-the-art NeRF-based methods in preserving high-frequency details and ensuring consistent body contours. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to create detailed 3D models of humans from videos taken with just one camera. The goal is to make sure the model looks like a real person, not just a rough outline. To do this, the researchers developed a special kind of computer program that can separate the video into two parts: the shape and pose (position) of the body, and the small details like wrinkles. This allows the program to create a 3D model that is both accurate and detailed. The results show that this method works better than others in preserving the fine details and keeping the overall shape consistent. |
