Kiss3DGen：重新利用圖像擴散模型進行3D資產生成

Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation

March 3, 2025

作者: Jiantao Lin, Xin Yang, Meixi Chen, Yingjie Xu, Dongyu Yan, Leyi Wu, Xinli Xu, Lie XU, Shunsi Zhang, Ying-Cong Chen

cs.AI

摘要

擴散模型在生成二維圖像方面已取得巨大成功。然而，三維內容生成的質量和泛化能力仍然有限。最先進的方法通常需要大規模的三維資產進行訓練，這些資產的收集具有挑戰性。在本研究中，我們介紹了Kiss3DGen（Keep It Simple and Straightforward in 3D Generation），這是一個高效的框架，通過重新利用訓練良好的二維圖像擴散模型來生成、編輯和增強三維物體。具體來說，我們微調了一個擴散模型來生成「三維捆綁圖像」，這是一種由多視角圖像及其對應的法線圖組成的平鋪表示。法線圖隨後用於重建三維網格，而多視角圖像則提供紋理映射，從而生成完整的三維模型。這種簡單的方法有效地將三維生成問題轉化為二維圖像生成任務，最大限度地利用了預訓練擴散模型中的知識。此外，我們展示了Kiss3DGen模型與各種擴散模型技術的兼容性，使其能夠實現三維編輯、網格和紋理增強等高級功能。通過大量實驗，我們證明了該方法的有效性，展示了其高效生成高質量三維模型的能力。

English

Diffusion models have achieved great success in generating 2D images. However, the quality and generalizability of 3D content generation remain limited. State-of-the-art methods often require large-scale 3D assets for training, which are challenging to collect. In this work, we introduce Kiss3DGen (Keep It Simple and Straightforward in 3D Generation), an efficient framework for generating, editing, and enhancing 3D objects by repurposing a well-trained 2D image diffusion model for 3D generation. Specifically, we fine-tune a diffusion model to generate ''3D Bundle Image'', a tiled representation composed of multi-view images and their corresponding normal maps. The normal maps are then used to reconstruct a 3D mesh, and the multi-view images provide texture mapping, resulting in a complete 3D model. This simple method effectively transforms the 3D generation problem into a 2D image generation task, maximizing the utilization of knowledge in pretrained diffusion models. Furthermore, we demonstrate that our Kiss3DGen model is compatible with various diffusion model techniques, enabling advanced features such as 3D editing, mesh and texture enhancement, etc. Through extensive experiments, we demonstrate the effectiveness of our approach, showcasing its ability to produce high-quality 3D models efficiently.