Consistency^2: Consistent and Fast 3D Painting with Latent Consistency
  Models
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.
66718a20d1722d7e008c8b84
Tianfu Wang
claimed_verified
2025-05-16T07:13:42.879Z
633c25e000732349209d4e79
/avatars/0fd853f6cbfcacc98035364585d58f2c.svg
Tianfu Wang
user
Tianfwang
66718a20d1722d7e008c8b85
Anton Obukhov
admin_assigned
2024-06-18T16:27:46.336Z
62f93abbc4817cfc0756b6f8
https://cdn-avatars.huggingface.co/v1/production/uploads/62f93abbc4817cfc0756b6f8/rGYLaq-rmoJJYotkC1VXk.jpeg
Anton Obukhov
user
toshas
66718a20d1722d7e008c8b86
Konrad Schindler
claimed_verified
2025-05-15T13:45:51.007Z
6750649b7edd6a98a1bbcd06
/avatars/ba27f12d0333cf2d400d4405af7efe97.svg
Konrad Schindler
user
konradschindler
Consistency^2: Consistent and Fast 3D Painting with Latent Consistency
  Models
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.
A Latent Consistency Model is proposed to accelerate and improve 3D painting methods, demonstrating strong preference in evaluations compared to existing techniques.
denoising diffusion latent space Latent Consistency Model Objaverse dataset