title: Consistency^2: Consistent and Fast 3D Painting with Latent Consistency
    Models
summary: Generative 3D Painting is among the top productivity boosters in
  high-resolution 3D asset management and recycling. Ever since text-to-image
  models became accessible for inference on consumer hardware, the performance of
  3D Painting methods has consistently improved and is currently close to
  plateauing. At the core of most such models lies denoising diffusion in the
  latent space, an inherently time-consuming iterative process. Multiple
  techniques have been developed recently to accelerate generation and reduce
  sampling iterations by orders of magnitude. Designed for 2D generative imaging,
  these techniques do not come with recipes for lifting them into 3D. In this
  paper, we address this shortcoming by proposing a Latent Consistency Model
  (LCM) adaptation for the task at hand. We analyze the strengths and weaknesses
  of the proposed model and evaluate it quantitatively and qualitatively. Based
  on the Objaverse dataset samples study, our 3D painting method attains strong
  preference in all evaluations. Source code is available at
  https://github.com/kongdai123/consistency2.
paper_authors._id: 66718a20d1722d7e008c8b84
paper_authors.name: Tianfu Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-16T07:13:42.879Z
paper_authors._id: 633c25e000732349209d4e79
paper_authors.avatarUrl: /avatars/0fd853f6cbfcacc98035364585d58f2c.svg
paper_authors.fullname: Tianfu Wang
paper_authors.type: user
paper_authors.user: Tianfwang
paper_authors._id: 66718a20d1722d7e008c8b85
paper_authors.name: Anton Obukhov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-18T16:27:46.336Z
paper_authors._id: 62f93abbc4817cfc0756b6f8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62f93abbc4817cfc0756b6f8/rGYLaq-rmoJJYotkC1VXk.jpeg
paper_authors.fullname: Anton Obukhov
paper_authors.type: user
paper_authors.user: toshas
paper_authors._id: 66718a20d1722d7e008c8b86
paper_authors.name: Konrad Schindler
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-15T13:45:51.007Z
paper_authors._id: 6750649b7edd6a98a1bbcd06
paper_authors.avatarUrl: /avatars/ba27f12d0333cf2d400d4405af7efe97.svg
paper_authors.fullname: Konrad Schindler
paper_authors.type: user
paper_authors.user: konradschindler
paper_title: Consistency^2: Consistent and Fast 3D Painting with Latent Consistency
    Models
paper_summary: Generative 3D Painting is among the top productivity boosters in
  high-resolution 3D asset management and recycling. Ever since text-to-image
  models became accessible for inference on consumer hardware, the performance of
  3D Painting methods has consistently improved and is currently close to
  plateauing. At the core of most such models lies denoising diffusion in the
  latent space, an inherently time-consuming iterative process. Multiple
  techniques have been developed recently to accelerate generation and reduce
  sampling iterations by orders of magnitude. Designed for 2D generative imaging,
  these techniques do not come with recipes for lifting them into 3D. In this
  paper, we address this shortcoming by proposing a Latent Consistency Model
  (LCM) adaptation for the task at hand. We analyze the strengths and weaknesses
  of the proposed model and evaluate it quantitatively and qualitatively. Based
  on the Objaverse dataset samples study, our 3D painting method attains strong
  preference in all evaluations. Source code is available at
  https://github.com/kongdai123/consistency2.
paper_ai_summary: A Latent Consistency Model is proposed to accelerate and improve 3D painting methods, demonstrating strong preference in evaluations compared to existing techniques.
paper_ai_keywords: denoising diffusion latent space Latent Consistency Model Objaverse dataset