Summary of Free-form Generation Enhances Challenging Clothed Human Modeling, by Hang Ye et al.
Free-form Generation Enhances Challenging Clothed Human Modeling
by Hang Ye, Xiaoxuan Ma, Hai Ci, Wentao Zhu, Yizhou Wang
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); 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 This paper proposes a novel hybrid framework to model realistic animated human avatars with accurate pose-dependent clothing deformations. The approach leverages Linear Blend Skinning (LBS) for minimally-clothed regions, while introducing a free-form, part-aware generator for loose clothing areas. The method segments the human body into unclothed, deformed, and generated regions, allowing it to capture intricate geometric details of challenging loose clothing like skirts and dresses. Experimental results on a benchmark dataset featuring loose clothing demonstrate state-of-the-art performance with superior visual fidelity and realism, particularly in challenging cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating realistic computer-generated humans that look like they’re wearing clothes that move naturally when they walk or run. Right now, computers have trouble making clothes look good when they’re far away from the body, like a long dress flowing behind someone. The researchers came up with a new way to make these animated characters by dividing their bodies into different parts and using special techniques for each part. They found that this method can create really realistic-looking animations of people wearing loose clothing. |
