扩散模型是人类网络恢复的高效数据生成器
IEEE transactions on pattern analysis and machine intelligence
|November 18, 2025
概括
这项研究介绍了HumanWild,一个使用生成扩散模型创建合成3D人类姿势和形状 (HPS) 数据的新管道. 这种方法克服了现有数据集的局限性,使得在不同的环境中能够更好地进行现实世界HPS估计.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 3D人体姿势和形状估计 3D人体姿势和形状估计
背景情况:
- 当前的3D人体姿势和形状 (HPS) 方法依赖于室内移动捕捉或计算机图形 (CG) 数据,缺乏现实世界的多样性.
- 现有的数据集未能捕捉到真实的人类身份和野生背景场景,阻碍了概括.
研究的目的:
- 开发一个新的数据生成管道,HumanWild,用于创建具有准确网格注释的合成3D人类图像.
- 通过利用生成模型,提高3D HPS方法在各种现实场景上的概括性能.
主要方法:
- 使用扩散模型和定制的ControlNet来生成人类图像和3D网格注释.
- 采用3D参数模型 (SMPL-X) 来精确生成2D关键点,深度图和表面正常.
- 集成的基础细分和2D顶点估计用于数据过和标签纠正.
主要成果:
- 成功生成了大规模的野生人类图像,带有高质量的3D网格注释.
- 通过对HPS基准进行全面的废除研究,证明了合成数据的有效性.
- 展示了管道对于人类交互和广角捕捉等任务的灵活性.
结论:
- 生成模型为3D HPS提供了对CG数据的强大,互补的方法.
- 人类野生管道有效地扩展数据生成,用于野生3D人类恢复.
- 这项工作在现实场景中促进了更强大和更通用的3D人类姿势和形状估计.
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