一致性的平方：具有潜在一致性模型的一致且快速的3D绘画

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

June 17, 2024

作者: Tianfu Wang, Anton Obukhov, Konrad Schindler

cs.AI

摘要

生成式3D绘画是高分辨率3D资产管理和回收中最重要的生产力提升者之一。自从文本到图像模型可以在消费者硬件上进行推断以来，3D绘画方法的性能不断提高，目前已接近平稳状态。大多数这类模型的核心是潜空间中的去噪扩散，这是一个固有的耗时迭代过程。最近已经开发出多种技术来加速生成并将采样迭代次数减少数个数量级。这些技术是为2D生成成像设计的，但并没有提供将其转化为3D的方法。在本文中，我们通过提出适用于当前任务的潜在一致性模型（LCM）来解决这一不足。我们定量和定性地分析了所提出模型的优势和劣势。基于Objaverse数据集样本研究，我们的3D绘画方法在所有评估中均表现出较强的偏好。源代码可在https://github.com/kongdai123/consistency2找到。

English

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.