title: Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation
summary: Diffusion models have achieved great success in generating 2D images.
  However, the quality and generalizability of 3D content generation remain
  limited. State-of-the-art methods often require large-scale 3D assets for
  training, which are challenging to collect. In this work, we introduce
  Kiss3DGen (Keep It Simple and Straightforward in 3D Generation), an efficient
  framework for generating, editing, and enhancing 3D objects by repurposing a
  well-trained 2D image diffusion model for 3D generation. Specifically, we
  fine-tune a diffusion model to generate ''3D Bundle Image'', a tiled
  representation composed of multi-view images and their corresponding normal
  maps. The normal maps are then used to reconstruct a 3D mesh, and the
  multi-view images provide texture mapping, resulting in a complete 3D model.
  This simple method effectively transforms the 3D generation problem into a 2D
  image generation task, maximizing the utilization of knowledge in pretrained
  diffusion models. Furthermore, we demonstrate that our Kiss3DGen model is
  compatible with various diffusion model techniques, enabling advanced features
  such as 3D editing, mesh and texture enhancement, etc. Through extensive
  experiments, we demonstrate the effectiveness of our approach, showcasing its
  ability to produce high-quality 3D models efficiently.
paper_authors._id: 67c691673ff65c55829685a0
paper_authors.name: Jiantao Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-04T10:52:36.682Z
paper_authors._id: 6332e2689bf698ce68a22e8c
paper_authors.avatarUrl: /avatars/c1922acfda2e6d2fe7b03194a404eb10.svg
paper_authors.fullname: JIANTAO LIN
paper_authors.type: user
paper_authors.user: LTT
paper_authors._id: 67c691673ff65c55829685a1
paper_authors.name: Xin Yang
paper_authors._id: 67c691673ff65c55829685a2
paper_authors.name: Meixi Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-04T10:52:44.047Z
paper_authors._id: 63641f09a53b71b7a1b02955
paper_authors.avatarUrl: /avatars/2f43703cbbc56f3e3f98090f44bccfe6.svg
paper_authors.fullname: Meixi Chen
paper_authors.type: user
paper_authors.user: MeixiChen
paper_authors._id: 67c691673ff65c55829685a3
paper_authors.name: Yingjie Xu
paper_authors._id: 67c691673ff65c55829685a4
paper_authors.name: Dongyu Yan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-04T08:34:56.252Z
paper_authors._id: 64049ae20ab5e22719f35103
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1678023295407-noauth.jpeg
paper_authors.fullname: Dongyu Yan
paper_authors.type: user
paper_authors.user: StarYDY
paper_authors._id: 67c691673ff65c55829685a5
paper_authors.name: Leyi Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-07T13:34:04.450Z
paper_authors._id: 6622f3e1c80be2cc569fb5e1
paper_authors.avatarUrl: /avatars/f4bacc6e090ec6e6d9f89b279783f1bd.svg
paper_authors.fullname: LeyiWu
paper_authors.type: user
paper_authors.user: YUEVII
paper_authors._id: 67c691673ff65c55829685a6
paper_authors.name: Xinli Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-04T10:53:15.555Z
paper_authors._id: 64b4ab62eec33e27dcd733b5
paper_authors.avatarUrl: /avatars/0a9bf220c9a5efe7279f9b287b087d36.svg
paper_authors.fullname: Xinli XU
paper_authors.type: user
paper_authors.user: Xxlbigbrother
paper_authors._id: 67c691673ff65c55829685a7
paper_authors.name: Lie XU
paper_authors._id: 67c691673ff65c55829685a8
paper_authors.name: Shunsi Zhang
paper_authors._id: 67c691673ff65c55829685a9
paper_authors.name: Ying-Cong Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-04T10:53:33.509Z
paper_authors._id: 655cba1d87b67834000590e8
paper_authors.avatarUrl: /avatars/3bd43b7c9351f65b8f38f4c8237a0146.svg
paper_authors.fullname: Yingcong Chen
paper_authors.type: user
paper_authors.user: yingcongchen
paper_title: Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation
paper_summary: Diffusion models have achieved great success in generating 2D images.
  However, the quality and generalizability of 3D content generation remain
  limited. State-of-the-art methods often require large-scale 3D assets for
  training, which are challenging to collect. In this work, we introduce
  Kiss3DGen (Keep It Simple and Straightforward in 3D Generation), an efficient
  framework for generating, editing, and enhancing 3D objects by repurposing a
  well-trained 2D image diffusion model for 3D generation. Specifically, we
  fine-tune a diffusion model to generate ''3D Bundle Image'', a tiled
  representation composed of multi-view images and their corresponding normal
  maps. The normal maps are then used to reconstruct a 3D mesh, and the
  multi-view images provide texture mapping, resulting in a complete 3D model.
  This simple method effectively transforms the 3D generation problem into a 2D
  image generation task, maximizing the utilization of knowledge in pretrained
  diffusion models. Furthermore, we demonstrate that our Kiss3DGen model is
  compatible with various diffusion model techniques, enabling advanced features
  such as 3D editing, mesh and texture enhancement, etc. Through extensive
  experiments, we demonstrate the effectiveness of our approach, showcasing its
  ability to produce high-quality 3D models efficiently.
paper_ai_summary: Kiss3DGen leverages a 2D diffusion model to generate high-quality 3D models by fine-tuning for 3D bundle images and using normal maps for mesh reconstruction.
paper_ai_keywords: diffusion models 3D generation 3D Bundle Image normal maps 3D mesh texture mapping 3D editing mesh enhancement texture enhancement