title: StdGEN: Semantic-Decomposed 3D Character Generation from Single Images
summary: 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
paper_authors._id: 6731689982e5f7370597d594
paper_authors.name: Yuze He
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-14T10:09:48.424Z
paper_authors._id: 64c903957b4d0d947ce86bc6
paper_authors.avatarUrl: /avatars/61d70a3ba00c83a5950f5c909a1a06f8.svg
paper_authors.fullname: Yuze He
paper_authors.type: user
paper_authors.user: hyz317
paper_authors._id: 6731689982e5f7370597d595
paper_authors.name: Yanning Zhou
paper_authors._id: 6731689982e5f7370597d596
paper_authors.name: Wang Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-02T09:33:49.351Z
paper_authors._id: 66054d519af76210c2ee4b8e
paper_authors.avatarUrl: /avatars/c1854765b1e45ec33602b2cb9443f82a.svg
paper_authors.fullname: Wang Zhao
paper_authors.type: user
paper_authors.user: thuzhaowang
paper_authors._id: 6731689982e5f7370597d597
paper_authors.name: Zhongkai Wu
paper_authors._id: 6731689982e5f7370597d598
paper_authors.name: Kaiwen Xiao
paper_authors._id: 6731689982e5f7370597d599
paper_authors.name: Wei Yang
paper_authors._id: 6731689982e5f7370597d59a
paper_authors.name: Yong-Jin Liu
paper_authors._id: 6731689982e5f7370597d59b
paper_authors.name: Xiao Han
paper_title: StdGEN: Semantic-Decomposed 3D Character Generation from Single Images
paper_summary: 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
paper_ai_summary: 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.
paper_ai_keywords: 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