LHM: Large Animatable Human Reconstruction Model from a Single Image in
  Seconds
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.
67dacb439c49701f604e4257
Lingteng Qiu
67dacb439c49701f604e4258
Xiaodong Gu
67dacb439c49701f604e4259
Peihao Li
claimed_verified
2025-03-24T08:54:46.948Z
67df7705bfe6948a4ee06d6e
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/TgYz2yhNk61zc0lkde_B2.png
Peihao Li
user
lipeihao
67dacb439c49701f604e425a
Qi Zuo
claimed_verified
2025-03-20T10:46:05.121Z
64d0d72e15b26cc7f704a60f
/avatars/f450013fb2b204f1422e614629914248.svg
Qi Zuo
user
DyrusQZ
67dacb439c49701f604e425b
Weichao Shen
67dacb439c49701f604e425c
Junfei Zhang
67dacb439c49701f604e425d
Kejie Qiu
67dacb439c49701f604e425e
Weihao Yuan
67dacb439c49701f604e425f
Guanying Chen
67dacb439c49701f604e4260
Zilong Dong
67dacb439c49701f604e4261
Liefeng Bo
LHM: Large Animatable Human Reconstruction Model from a Single Image in
  Seconds
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.
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.
3D Gaussian splatting multimodal transformer attention mechanism head feature pyramid animatable human reconstruction