OccFusion:使被封闭的人类具有生成扩散先验
Adam Sun1, Tiange Xiang1, Scott Delp1
1Stanford University.
Advances in neural information processing systems
|June 27, 2025
概括
OccFusion有效地将人类从部分视图中呈现出来,使用3D高斯斯喷射和扩散模型. 这种方法克服了常见的现实世界的屏蔽,用于高保真度的人类染.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 机器学习 机器学习
背景情况:
- 现有的人类染方法通常假定完全可见性,这是不现实的.
- 现实世界的场景经常涉及遮蔽,导致人类在输入数据中的部分可见性.
研究的目的:
- 为了开发一个高效和高保真的人类染方法,用于场景与闭塞.
- 为了从部分可见的输入中实现完整的人类几何重建.
主要方法:
- OccFusion使用高效的3D高斯喷,由预训练的2D扩散模型监督.
- 一个三阶段的管道涉及面具生成,3D高斯优化与分数蒸采样 (SDS),以及在上下文中绘制以进行改进.
主要成果:
- OccFusion成功地从部分可见面具中生成了完整的人体几何.
- 该方法在像ZJU-MoCap和OcMotion.com这样的基准数据集上实现了对被遮蔽的人类进行染的先进性能.
结论:
- 在存在闭塞的情况下,OccFusion提供了一种有效的解决方案,用于高保真度的人体染.
- 拟议的管道展示了结合3D高斯喷射和扩散模型的潜力,以完成具有挑战性的计算机视觉任务.
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