title: ThemeStation: Generating Theme-Aware 3D Assets from Few Exemplars
summary: Real-world applications often require a large gallery of 3D assets that share
  a consistent theme. While remarkable advances have been made in general 3D
  content creation from text or image, synthesizing customized 3D assets
  following the shared theme of input 3D exemplars remains an open and
  challenging problem. In this work, we present ThemeStation, a novel approach
  for theme-aware 3D-to-3D generation. ThemeStation synthesizes customized 3D
  assets based on given few exemplars with two goals: 1) unity for generating 3D
  assets that thematically align with the given exemplars and 2) diversity for
  generating 3D assets with a high degree of variations. To this end, we design a
  two-stage framework that draws a concept image first, followed by a
  reference-informed 3D modeling stage. We propose a novel dual score
  distillation (DSD) loss to jointly leverage priors from both the input
  exemplars and the synthesized concept image. Extensive experiments and user
  studies confirm that ThemeStation surpasses prior works in producing diverse
  theme-aware 3D models with impressive quality. ThemeStation also enables
  various applications such as controllable 3D-to-3D generation.
paper_authors._id: 6600fb883d247974ba253e90
paper_authors.name: Zhenwei Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-25T16:21:54.715Z
paper_authors._id: 6305e9471e731505a76b9099
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6305e9471e731505a76b9099/JikVYIMZWoeKMd99Ruvej.jpeg
paper_authors.fullname: Zhenwei WANG (Zavier)
paper_authors.type: user
paper_authors.user: ZhenweiWang
paper_authors._id: 6600fb883d247974ba253e91
paper_authors.name: Tengfei Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-25T10:55:00.012Z
paper_authors._id: 62e7c26236a8e8a827ff0891
paper_authors.avatarUrl: /avatars/15648407233bf5757262dbc04464d02e.svg
paper_authors.fullname: Tengfei Wang
paper_authors.type: user
paper_authors.user: tfwang
paper_authors._id: 6600fb883d247974ba253e92
paper_authors.name: Gerhard Hancke
paper_authors._id: 6600fb883d247974ba253e93
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-25T10:55:21.146Z
paper_authors._id: 62ab1ac1d48b4d8b048a3473
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
paper_authors.fullname: Ziwei Liu
paper_authors.type: user
paper_authors.user: liuziwei7
paper_authors._id: 6600fb883d247974ba253e94
paper_authors.name: Rynson W. H. Lau
paper_title: ThemeStation: Generating Theme-Aware 3D Assets from Few Exemplars
paper_summary: Real-world applications often require a large gallery of 3D assets that share
  a consistent theme. While remarkable advances have been made in general 3D
  content creation from text or image, synthesizing customized 3D assets
  following the shared theme of input 3D exemplars remains an open and
  challenging problem. In this work, we present ThemeStation, a novel approach
  for theme-aware 3D-to-3D generation. ThemeStation synthesizes customized 3D
  assets based on given few exemplars with two goals: 1) unity for generating 3D
  assets that thematically align with the given exemplars and 2) diversity for
  generating 3D assets with a high degree of variations. To this end, we design a
  two-stage framework that draws a concept image first, followed by a
  reference-informed 3D modeling stage. We propose a novel dual score
  distillation (DSD) loss to jointly leverage priors from both the input
  exemplars and the synthesized concept image. Extensive experiments and user
  studies confirm that ThemeStation surpasses prior works in producing diverse
  theme-aware 3D models with impressive quality. ThemeStation also enables
  various applications such as controllable 3D-to-3D generation.
paper_ai_summary: ThemeStation generates high-quality, theme-aware 3D assets from few exemplars using a dual score distillation loss in a two-stage framework.
paper_ai_keywords: theme-aware 3D-to-3D generation dual score distillation concept image reference-informed 3D modeling theme-aware 3D models controllable 3D-to-3D generation