单独的Gen:基于语义组件的3D字符生成从单个图像
IEEE transactions on visualization and computer graphics
|January 12, 2026
概括
通过将语义组件分开,SeparateGen可以从单个图像中创建高质量的3D字符. 这种新的方法解决了网状纠,使3D资产的操纵和动画更容易.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 人工智能的人工智能
背景情况:
- 从单个图像生成详细的3D字符是很困难的,因为语义组件纠.
- 现有的方法往往导致糟糕的网格拓,限制了动画和操纵中的可用性.
研究的目的:
- 介绍SeparateGen,这是一个从单个图像中生成高质量的3D字符的框架.
- 明确地将字符重建成不同的语义组件 (身体,衣服,头发,鞋子).
主要方法:
- 使用多视图扩散模型来实现一致的规范A-pose图像生成.
- 采用一种新的组件意识重建模型 (SC-LRM) 进行高保真组件分解和重建.
- 介绍SC-Anime,这是一个大规模的数据集,包含了7,580个动漫风格的3D角色,并附有组件级注释.
主要成果:
- 在重建质量和多视图一致性方面,SeparateGen显著超过了最先进的方法.
- 基于组件的方法有效地解决了网状纠问题.
- 生成的字符适用于无装配和资产再利用.
结论:
- SeparateGen推进了从单个图像生成应用程序准备的3D字符的生成.
- SC-Anime数据集和代码将公开发布,以促进进一步的研究.
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