FRESA:Feedforward Reconstruction of Personalized Skinned Avatars from
  Few Images
Authors: name: Rong Wang, name: Fabian Prada, name: Ziyan Wang, name: Zhongshi Jiang, name: Chengxiang Yin, name: Junxuan Li, name: Shunsuke Saito, name: Igor Santesteban, name: Javier Romero, name: Rohan Joshi, name: Hongdong Li, name: Jason Saragih, name: Yaser Sheikh

We present a novel method for reconstructing personalized 3D human avatars
with realistic animation from only a few images. Due to the large variations in
body shapes, poses, and cloth types, existing methods mostly require hours of
per-subject optimization during inference, which limits their practical
applications. In contrast, we learn a universal prior from over a thousand
clothed humans to achieve instant feedforward generation and zero-shot
generalization. Specifically, instead of rigging the avatar with shared
skinning weights, we jointly infer personalized avatar shape, skinning weights,
and pose-dependent deformations, which effectively improves overall geometric
fidelity and reduces deformation artifacts. Moreover, to normalize pose
variations and resolve coupled ambiguity between canonical shapes and skinning
weights, we design a 3D canonicalization process to produce pixel-aligned
initial conditions, which helps to reconstruct fine-grained geometric details.
We then propose a multi-frame feature aggregation to robustly reduce artifacts
introduced in canonicalization and fuse a plausible avatar preserving
person-specific identities. Finally, we train the model in an end-to-end
framework on a large-scale capture dataset, which contains diverse human
subjects paired with high-quality 3D scans. Extensive experiments show that our
method generates more authentic reconstruction and animation than
state-of-the-arts, and can be directly generalized to inputs from casually
taken phone photos. Project page and code is available at
https://github.com/rongakowang/FRESA.