title: FRESA:Feedforward Reconstruction of Personalized Skinned Avatars from
    Few Images
summary: 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.
paper_authors._id: 67e3f27a0d1715115dcd5b28
paper_authors.name: Rong Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-27T09:04:51.500Z
paper_authors._id: 66902102ba4da069637ee990
paper_authors.avatarUrl: /avatars/edae63fcd1e9d32f1674b6d0fff21ddd.svg
paper_authors.fullname: Rong Wang
paper_authors.type: user
paper_authors.user: rongwang089
paper_authors._id: 67e3f27a0d1715115dcd5b29
paper_authors.name: Fabian Prada
paper_authors._id: 67e3f27a0d1715115dcd5b2a
paper_authors.name: Ziyan Wang
paper_authors._id: 67e3f27a0d1715115dcd5b2b
paper_authors.name: Zhongshi Jiang
paper_authors._id: 67e3f27a0d1715115dcd5b2c
paper_authors.name: Chengxiang Yin
paper_authors._id: 67e3f27a0d1715115dcd5b2d
paper_authors.name: Junxuan Li
paper_authors._id: 67e3f27a0d1715115dcd5b2e
paper_authors.name: Shunsuke Saito
paper_authors._id: 67e3f27a0d1715115dcd5b2f
paper_authors.name: Igor Santesteban
paper_authors._id: 67e3f27a0d1715115dcd5b30
paper_authors.name: Javier Romero
paper_authors._id: 67e3f27a0d1715115dcd5b31
paper_authors.name: Rohan Joshi
paper_authors._id: 67e3f27a0d1715115dcd5b32
paper_authors.name: Hongdong Li
paper_authors._id: 67e3f27a0d1715115dcd5b33
paper_authors.name: Jason Saragih
paper_authors._id: 67e3f27a0d1715115dcd5b34
paper_authors.name: Yaser Sheikh
paper_title: FRESA:Feedforward Reconstruction of Personalized Skinned Avatars from
    Few Images
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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