title: DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation
    for High-quality 3D Asset Creation
summary: Procedural Content Generation (PCG) is powerful in creating high-quality 3D
  contents, yet controlling it to produce desired shapes is difficult and often
  requires extensive parameter tuning. Inverse Procedural Content Generation aims
  to automatically find the best parameters under the input condition. However,
  existing sampling-based and neural network-based methods still suffer from
  numerous sample iterations or limited controllability. In this work, we present
  DI-PCG, a novel and efficient method for Inverse PCG from general image
  conditions. At its core is a lightweight diffusion transformer model, where PCG
  parameters are directly treated as the denoising target and the observed images
  as conditions to control parameter generation. DI-PCG is efficient and
  effective. With only 7.6M network parameters and 30 GPU hours to train, it
  demonstrates superior performance in recovering parameters accurately, and
  generalizing well to in-the-wild images. Quantitative and qualitative
  experiment results validate the effectiveness of DI-PCG in inverse PCG and
  image-to-3D generation tasks. DI-PCG offers a promising approach for efficient
  inverse PCG and represents a valuable exploration step towards a 3D generation
  path that models how to construct a 3D asset using parametric models.
paper_authors._id: 6764da71bdc5692a8d6bccf6
paper_authors.name: Wang Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-20T08:32:57.404Z
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: 6764da71bdc5692a8d6bccf7
paper_authors.name: Yan-Pei Cao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-20T09:27:44.668Z
paper_authors._id: 638066faf022c8a5803f7eb8
paper_authors.avatarUrl: /avatars/4cfd699c3f6c5461b12b7dc5e3fe183d.svg
paper_authors.fullname: Yanpei Cao
paper_authors.type: user
paper_authors.user: pookiefoof
paper_authors._id: 6764da71bdc5692a8d6bccf8
paper_authors.name: Jiale Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-20T09:28:27.765Z
paper_authors._id: 62c695829db11473f08af1cd
paper_authors.avatarUrl: /avatars/cacb54077892a44aef81454dc107df4f.svg
paper_authors.fullname: Jiale Xu
paper_authors.type: user
paper_authors.user: bluestyle97
paper_authors._id: 6764da71bdc5692a8d6bccf9
paper_authors.name: Yuejiang Dong
paper_authors._id: 6764da71bdc5692a8d6bccfa
paper_authors.name: Ying Shan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-20T09:28:06.210Z
paper_authors._id: 63ca3ddc04c979828310bfcb
paper_authors.avatarUrl: /avatars/615e0d8622950b4408b40d550f02a894.svg
paper_authors.fullname: Ying Shan
paper_authors.type: user
paper_authors.user: yshan2u
paper_title: DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation
    for High-quality 3D Asset Creation
paper_summary: Procedural Content Generation (PCG) is powerful in creating high-quality 3D
  contents, yet controlling it to produce desired shapes is difficult and often
  requires extensive parameter tuning. Inverse Procedural Content Generation aims
  to automatically find the best parameters under the input condition. However,
  existing sampling-based and neural network-based methods still suffer from
  numerous sample iterations or limited controllability. In this work, we present
  DI-PCG, a novel and efficient method for Inverse PCG from general image
  conditions. At its core is a lightweight diffusion transformer model, where PCG
  parameters are directly treated as the denoising target and the observed images
  as conditions to control parameter generation. DI-PCG is efficient and
  effective. With only 7.6M network parameters and 30 GPU hours to train, it
  demonstrates superior performance in recovering parameters accurately, and
  generalizing well to in-the-wild images. Quantitative and qualitative
  experiment results validate the effectiveness of DI-PCG in inverse PCG and
  image-to-3D generation tasks. DI-PCG offers a promising approach for efficient
  inverse PCG and represents a valuable exploration step towards a 3D generation
  path that models how to construct a 3D asset using parametric models.
paper_ai_keywords: diffusion transformer model denoising target inverse PCG image-to-3D generation tasks parametric models