HuGDiffusion:可通用的单图像人体染通过3D高斯扩散
IEEE transactions on visualization and computer graphics
|October 24, 2025
概括
通过HuGDiffusion,可以从单个图像中使用3D高斯喷射 (3DGS) 来合成人类角色的新视图. 这种基于扩散的方法克服了多视图输入的局限性,以实现现实的人类角色染.
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:
- 人类角色的新视图合成 (NVS) 通常需要多个校准的图像或视频.
- 现实世界的场景经常缺乏这样的受控输入数据,限制了现有的方法.
- 从单个图像生成3D表示是一个重大挑战.
研究的目的:
- 从单视图图像中开发一种可通用的管道,用于从单视图图像中合成人类角色的新视图.
- 为了利用扩散模型来生成基于人类先验的3D高斯分裂 (3DGS) 属性.
- 克服依赖于多视图或视频输入的现有NVS方法的局限性.
主要方法:
- 基于扩散的框架用于从单个人类图像中生成3DGS属性.
- 以人为中心的特征提取用于导出扩散模型的条件信号.
- 一个多阶段的生成策略旨在优化学习不同的3DGS属性.
- 代理地面真实3D高斯属性被构建为提供高质量的监督信号.
主要成果:
- 在人类特征NVS的最先进方法中,HuGDiffusion表现出了显著的性能改善.
- 该方法成功地从单个输入图像中合成了新的视图,即使是未知的摄像头姿势.
- 管道显示了不同的人类角色和姿势的概括性.
结论:
- HuGDiffusion提供了一种有效的解决方案,用于使用单视图输入对人类角色进行新的视图合成.
- 提出的基于扩散的方法与多阶段属性生成推进了3D字符重建领域.
- 公开可用的代码有助于进一步研究和应用3D人类建模和染.
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