FRESA:Feedforward Reconstruction of Personalized Skinned Avatars from
  Few Images
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.
67e3f27a0d1715115dcd5b28
Rong Wang
claimed_verified
2025-03-27T09:04:51.500Z
66902102ba4da069637ee990
/avatars/edae63fcd1e9d32f1674b6d0fff21ddd.svg
Rong Wang
user
rongwang089
67e3f27a0d1715115dcd5b29
Fabian Prada
67e3f27a0d1715115dcd5b2a
Ziyan Wang
67e3f27a0d1715115dcd5b2b
Zhongshi Jiang
67e3f27a0d1715115dcd5b2c
Chengxiang Yin
67e3f27a0d1715115dcd5b2d
Junxuan Li
67e3f27a0d1715115dcd5b2e
Shunsuke Saito
67e3f27a0d1715115dcd5b2f
Igor Santesteban
67e3f27a0d1715115dcd5b30
Javier Romero
67e3f27a0d1715115dcd5b31
Rohan Joshi
67e3f27a0d1715115dcd5b32
Hongdong Li
67e3f27a0d1715115dcd5b33
Jason Saragih
67e3f27a0d1715115dcd5b34
Yaser Sheikh
FRESA:Feedforward Reconstruction of Personalized Skinned Avatars from
  Few Images
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.
Anew method for real-time 3D avatar reconstruction with animation from few images uses a universal prior for instant generation and infers personalized shape, skinning weights, and pose, improving geometric fidelity and handling pose variations.
3D human avatars real-time reconstruction universal prior instant feedforward generation personalized shape skinning weights pose-dependent deformations geometric fidelity deformation artifacts 3D canonicalization multi-frame feature aggregation end-to-end training large-scale capture dataset authentic reconstruction animation