Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5D Diffusion

• URL: http://arxiv.org/abs/2311.15980v2
• Date: Thu, 21 Mar 2024 14:21:58 GMT
• Title: Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5D Diffusion
• Authors: Yuanxun Lu, Jingyang Zhang, Shiwei Li, Tian Fang, David McKinnon, Yanghai Tsin, Long Quan, Xun Cao, Yao Yao,
• Abstract summary: Current methods for creating 3D content are time-consuming and lose generation diversity. In this work, we employ a multi-view 2.5D diffusion fine-tuned from a pre-trained 2D diffusion model. We show that our direct 2.5D generation with the specially-designed fusion scheme can achieve diverse, mode-seeking-free, and high-fidelity 3D content generation in only 10 seconds.
• Score: 32.13452288549591
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in generative AI have unveiled significant potential for the creation of 3D content. However, current methods either apply a pre-trained 2D diffusion model with the time-consuming score distillation sampling (SDS), or a direct 3D diffusion model trained on limited 3D data losing generation diversity. In this work, we approach the problem by employing a multi-view 2.5D diffusion fine-tuned from a pre-trained 2D diffusion model. The multi-view 2.5D diffusion directly models the structural distribution of 3D data, while still maintaining the strong generalization ability of the original 2D diffusion model, filling the gap between 2D diffusion-based and direct 3D diffusion-based methods for 3D content generation. During inference, multi-view normal maps are generated using the 2.5D diffusion, and a novel differentiable rasterization scheme is introduced to fuse the almost consistent multi-view normal maps into a consistent 3D model. We further design a normal-conditioned multi-view image generation module for fast appearance generation given the 3D geometry. Our method is a one-pass diffusion process and does not require any SDS optimization as post-processing. We demonstrate through extensive experiments that, our direct 2.5D generation with the specially-designed fusion scheme can achieve diverse, mode-seeking-free, and high-fidelity 3D content generation in only 10 seconds. Project page: https://nju-3dv.github.io/projects/direct25.

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