PocoLoco：宽松衣物下人体形状的点云扩散模型
PocoLoco: A Point Cloud Diffusion Model of Human Shape in Loose Clothing
AI总结：
PocoLoco提出首个无需模板的点云扩散模型，直接生成宽松衣物下姿态条件的人体形状，并发布75K点云数据集，支持补全与编辑应用。
摘要：
对人体化身进行建模，使其能够合理地随关节运动而变形，是一个活跃的研究领域。我们提出了PocoLoco——首个无需模板、基于点、以姿态为条件的生成模型，用于宽松衣物下的三维人体。我们的工作动机源于注意到大多数方法需要人体参数化模型来支撑姿态相关的变形。因此，它们仅限于建模与裸体拓扑相似的衣物，且不能很好地扩展到宽松衣物。少数尝试建模宽松衣物的方法通常需要规范化或UV参数化，并且需要解决显式估计变形衣物对应关系的挑战性问题。在这项工作中，我们将化身衣物变形表述为去噪扩散框架内的条件点云生成任务。关键的是，我们的框架直接操作于无序点云，消除了对参数化模型或衣物模板的需求。这也使得多种实际应用成为可能，例如点云补全和基于姿态的编辑——这些对于虚拟人体动画而言是重要功能。由于当前用于宽松衣物人体化身的数据集对于训练扩散模型而言太小，我们发布了一个数据集，包含两个受试者穿着宽松衣物执行各种姿态，总计75K个点云。通过为解决有效建模宽松衣物这一挑战性任务做出贡献，并扩展用于训练这些模型的可用数据，我们旨在为数字人体的进一步创新奠定基础。源代码可在https://github.com/sidsunny/pocoloco获取。
英文摘要：
Modeling a human avatar that can plausibly deform to articulations is an active area of research. We present PocoLoco -- the first template-free, point-based, pose-conditioned generative model for 3D humans in loose clothing. We motivate our work by noting that most methods require a parametric model of the human body to ground pose-dependent deformations. Consequently, they are restricted to modeling clothing that is topologically similar to the naked body and do not extend well to loose clothing. The few methods that attempt to model loose clothing typically require either canonicalization or a UV-parameterization and need to address the challenging problem of explicitly estimating correspondences for the deforming clothes. In this work, we formulate avatar clothing deformation as a conditional point-cloud generation task within the denoising diffusion framework. Crucially, our framework operates directly on unordered point clouds, eliminating the need for a parametric model or a clothing template. This also enables a variety of practical applications, such as point-cloud completion and pose-based editing -- important features for virtual human animation. As current datasets for human avatars in loose clothing are far too small for training diffusion models, we release a dataset of two subjects performing various poses in loose clothing with a total of 75K point clouds. By contributing towards tackling the challenging task of effectively modeling loose clothing and expanding the available data for training these models, we aim to set the stage for further innovation in digital humans. The source code is available at https://github.com/sidsunny/pocoloco .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
