DressRecon: Freeform 4D Human Reconstruction from Monocular Video
Authors: name: Jeff Tan, name: Donglai Xiang, name: Shubham Tulsiani, name: Deva Ramanan, name: Gengshan Yang

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/