Summary of Simavatar: Simulation-ready Avatars with Layered Hair and Clothing, by Xueting Li et al.
SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing
by Xueting Li, Ye Yuan, Shalini De Mello, Gilles Daviet, Jonathan Leaf, Miles Macklin, Jan Kautz, Umar Iqbal
First submitted to arxiv on: 12 Dec 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 The paper introduces SimAvatar, a framework for generating simulation-ready clothed 3D human avatars from text prompts. It addresses the challenge of representing hair and garment geometry in a way that combines established image diffusion models with simulation-readiness. The authors propose a two-stage approach using 3D generative models to generate garment mesh, body shape, and hair strands, followed by optimization for avatar appearance. They leverage prior knowledge from diffusion models and apply physics simulators to drive the avatar’s motion. The result is highly realistic avatars with vivid texture and dynamic motion. This paper contributes to the development of 3D human avatars for simulation applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a way to make 3D people from text descriptions that looks real and can be used in simulations. They use three models to create the body, clothes, and hair, then make it look good by adjusting small details. To make the avatar move like a real person, they use physics to simulate how clothes and hair behave. This is important because it’s hard to find a way to combine different parts of the avatar to make it look realistic. The result is 3D people that look very real and can be used in simulations. |
Keywords
» Artificial intelligence » Diffusion » Optimization
