title: LHM: Large Animatable Human Reconstruction Model from a Single Image in
    Seconds
summary: Animatable 3D human reconstruction from a single image is a challenging
  problem due to the ambiguity in decoupling geometry, appearance, and
  deformation. Recent advances in 3D human reconstruction mainly focus on static
  human modeling, and the reliance of using synthetic 3D scans for training
  limits their generalization ability. Conversely, optimization-based video
  methods achieve higher fidelity but demand controlled capture conditions and
  computationally intensive refinement processes. Motivated by the emergence of
  large reconstruction models for efficient static reconstruction, we propose LHM
  (Large Animatable Human Reconstruction Model) to infer high-fidelity avatars
  represented as 3D Gaussian splatting in a feed-forward pass. Our model
  leverages a multimodal transformer architecture to effectively encode the human
  body positional features and image features with attention mechanism, enabling
  detailed preservation of clothing geometry and texture. To further boost the
  face identity preservation and fine detail recovery, we propose a head feature
  pyramid encoding scheme to aggregate multi-scale features of the head regions.
  Extensive experiments demonstrate that our LHM generates plausible animatable
  human in seconds without post-processing for face and hands, outperforming
  existing methods in both reconstruction accuracy and generalization ability.
paper_authors._id: 67dacb439c49701f604e4257
paper_authors.name: Lingteng Qiu
paper_authors._id: 67dacb439c49701f604e4258
paper_authors.name: Xiaodong Gu
paper_authors._id: 67dacb439c49701f604e4259
paper_authors.name: Peihao Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-24T08:54:46.948Z
paper_authors._id: 67df7705bfe6948a4ee06d6e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/TgYz2yhNk61zc0lkde_B2.png
paper_authors.fullname: Peihao Li
paper_authors.type: user
paper_authors.user: lipeihao
paper_authors._id: 67dacb439c49701f604e425a
paper_authors.name: Qi Zuo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-20T10:46:05.121Z
paper_authors._id: 64d0d72e15b26cc7f704a60f
paper_authors.avatarUrl: /avatars/f450013fb2b204f1422e614629914248.svg
paper_authors.fullname: Qi Zuo
paper_authors.type: user
paper_authors.user: DyrusQZ
paper_authors._id: 67dacb439c49701f604e425b
paper_authors.name: Weichao Shen
paper_authors._id: 67dacb439c49701f604e425c
paper_authors.name: Junfei Zhang
paper_authors._id: 67dacb439c49701f604e425d
paper_authors.name: Kejie Qiu
paper_authors._id: 67dacb439c49701f604e425e
paper_authors.name: Weihao Yuan
paper_authors._id: 67dacb439c49701f604e425f
paper_authors.name: Guanying Chen
paper_authors._id: 67dacb439c49701f604e4260
paper_authors.name: Zilong Dong
paper_authors._id: 67dacb439c49701f604e4261
paper_authors.name: Liefeng Bo
paper_title: LHM: Large Animatable Human Reconstruction Model from a Single Image in
    Seconds
paper_summary: Animatable 3D human reconstruction from a single image is a challenging
  problem due to the ambiguity in decoupling geometry, appearance, and
  deformation. Recent advances in 3D human reconstruction mainly focus on static
  human modeling, and the reliance of using synthetic 3D scans for training
  limits their generalization ability. Conversely, optimization-based video
  methods achieve higher fidelity but demand controlled capture conditions and
  computationally intensive refinement processes. Motivated by the emergence of
  large reconstruction models for efficient static reconstruction, we propose LHM
  (Large Animatable Human Reconstruction Model) to infer high-fidelity avatars
  represented as 3D Gaussian splatting in a feed-forward pass. Our model
  leverages a multimodal transformer architecture to effectively encode the human
  body positional features and image features with attention mechanism, enabling
  detailed preservation of clothing geometry and texture. To further boost the
  face identity preservation and fine detail recovery, we propose a head feature
  pyramid encoding scheme to aggregate multi-scale features of the head regions.
  Extensive experiments demonstrate that our LHM generates plausible animatable
  human in seconds without post-processing for face and hands, outperforming
  existing methods in both reconstruction accuracy and generalization ability.
paper_ai_summary: LHM, a large-scale animatable human reconstruction model using 3D Gaussian splatting and a multimodal transformer, achieves high-fidelity avatar generation with detailed face identity preservation and multi-scale feature encoding.
paper_ai_keywords: 3D Gaussian splatting multimodal transformer attention mechanism head feature pyramid animatable human reconstruction