一致性的平方：具有潛在一致性模型的一致且快速的3D繪畫

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繪畫是高解析度3D資產管理和回收中最具生產力的工具之一。自從文本轉圖像模型能夠在消費者硬體上進行推論以來，3D繪畫方法的性能不斷提升，目前已接近平穩期。大多數這類模型的核心在於潛在空間中的去噪擴散，這是一個固有耗時的迭代過程。最近已開發了多種技術，可加速生成並將取樣迭代次數降低數量級。這些技術是為2D生成影像而設計的，並沒有提供將其擴展到3D的方法。在本文中，我們通過提出適用於此任務的潛在一致性模型（LCM）來解決這個缺陷。我們分析了所提出模型的優勢和劣勢，並進行了定量和定性評估。基於Objaverse資料集樣本研究，我們的3D繪畫方法在所有評估中均獲得了較高的偏好。原始碼可在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.