We present a method to reconstruct time-consistent human body models from monocular videos, focusing on extremely loose clothing or handheld object interactions. Prior work in human reconstruction is either limited to tight clothing with no object interactions, or requires calibrated multi-view captures or personalized template scans which are costly to collect at scale. Our key insight for high-quality yet flexible reconstruction is the careful combination of generic human priors about articulated body shape (learned from large-scale training data) with video-specific articulated "bag-of-bones" deformation (fit to a single video via test-time optimization). We accomplish this by learning a neural implicit model that disentangles body versus clothing deformations as separate motion model layers. To capture subtle geometry of clothing, we leverage image-based priors such as human body pose, surface normals, and optical flow during optimization. The resulting neural fields can be extracted into time-consistent meshes, or further optimized as explicit 3D Gaussians for high-fidelity interactive rendering. On datasets with highly challenging clothing deformations and object interactions, DressRecon yields higher-fidelity 3D reconstructions than prior art. Project page: https://jefftan969.github.io/dressrecon/

本研究解决了在单目视频中重建穿着松散衣物的人的时间一致的体模问题，填补了以往研究中对紧身衣物和物体交互的局限。通过结合从大量数据中学习的通用人体形状先验与视频特定的“骨骼袋”变形，该方法实现了高质量而灵活的重建。研究结果表明，DressRecon在处理复杂衣物变形和物体交互时，能够产生比之前方法更高保真的3D重建效果。

DressRecon：从单目视频中重建自由形式的4D人类模型