Consistency^2: Consistent and Fast 3D Painting with Latent Consistency Models

TL;DR

This paper introduces a novel Latent Consistency Model for fast and consistent 3D painting, significantly improving efficiency and quality in 3D asset generation by adapting techniques from 2D generative models.

Contribution

It presents the first adaptation of Latent Consistency Models for 3D painting, enabling faster and more consistent 3D asset generation compared to existing methods.

Findings

Achieves strong preference in evaluation studies.

Reduces sampling iterations significantly.

Attains high-quality 3D painting results.

Abstract

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.