StdGEN: Semantic-Decomposed 3D Character Generation from Single Images
We present StdGEN, an innovative pipeline for generating semantically
decomposed high-quality 3D characters from single images, enabling broad
applications in virtual reality, gaming, and filmmaking, etc. Unlike previous
methods which struggle with limited decomposability, unsatisfactory quality,
and long optimization times, StdGEN features decomposability, effectiveness and
efficiency; i.e., it generates intricately detailed 3D characters with
separated semantic components such as the body, clothes, and hair, in three
minutes. At the core of StdGEN is our proposed Semantic-aware Large
Reconstruction Model (S-LRM), a transformer-based generalizable model that
jointly reconstructs geometry, color and semantics from multi-view images in a
feed-forward manner. A differentiable multi-layer semantic surface extraction
scheme is introduced to acquire meshes from hybrid implicit fields
reconstructed by our S-LRM. Additionally, a specialized efficient multi-view
diffusion model and an iterative multi-layer surface refinement module are
integrated into the pipeline to facilitate high-quality, decomposable 3D
character generation. Extensive experiments demonstrate our state-of-the-art
performance in 3D anime character generation, surpassing existing baselines by
a significant margin in geometry, texture and decomposability. StdGEN offers
ready-to-use semantic-decomposed 3D characters and enables flexible
customization for a wide range of applications. Project page:
https://stdgen.github.io
6731689982e5f7370597d594
Yuze He
claimed_verified
2025-03-14T10:09:48.424Z
64c903957b4d0d947ce86bc6
/avatars/61d70a3ba00c83a5950f5c909a1a06f8.svg
Yuze He
user
hyz317
6731689982e5f7370597d595
Yanning Zhou
6731689982e5f7370597d596
Wang Zhao
claimed_verified
2024-12-02T09:33:49.351Z
66054d519af76210c2ee4b8e
/avatars/c1854765b1e45ec33602b2cb9443f82a.svg
Wang Zhao
user
thuzhaowang
6731689982e5f7370597d597
Zhongkai Wu
6731689982e5f7370597d598
Kaiwen Xiao
6731689982e5f7370597d599
Wei Yang
6731689982e5f7370597d59a
Yong-Jin Liu
6731689982e5f7370597d59b
Xiao Han
StdGEN: Semantic-Decomposed 3D Character Generation from Single Images
We present StdGEN, an innovative pipeline for generating semantically
decomposed high-quality 3D characters from single images, enabling broad
applications in virtual reality, gaming, and filmmaking, etc. Unlike previous
methods which struggle with limited decomposability, unsatisfactory quality,
and long optimization times, StdGEN features decomposability, effectiveness and
efficiency; i.e., it generates intricately detailed 3D characters with
separated semantic components such as the body, clothes, and hair, in three
minutes. At the core of StdGEN is our proposed Semantic-aware Large
Reconstruction Model (S-LRM), a transformer-based generalizable model that
jointly reconstructs geometry, color and semantics from multi-view images in a
feed-forward manner. A differentiable multi-layer semantic surface extraction
scheme is introduced to acquire meshes from hybrid implicit fields
reconstructed by our S-LRM. Additionally, a specialized efficient multi-view
diffusion model and an iterative multi-layer surface refinement module are
integrated into the pipeline to facilitate high-quality, decomposable 3D
character generation. Extensive experiments demonstrate our state-of-the-art
performance in 3D anime character generation, surpassing existing baselines by
a significant margin in geometry, texture and decomposability. StdGEN offers
ready-to-use semantic-decomposed 3D characters and enables flexible
customization for a wide range of applications. Project page:
https://stdgen.github.io
StdGEN generates high-quality, semantically decomposed 3D characters from single images using a transformer-based model and efficient diffusion techniques, outperforming existing methods in speed and quality.
Semantic-aware Large Reconstruction Model transformer-based model differentiable multi-layer semantic surface extraction hybrid implicit fields efficient multi-view diffusion model iterative multi-layer surface refinement module