BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion

• URL: http://arxiv.org/abs/2401.16764v3
• Date: Tue, 17 Sep 2024 16:28:40 GMT
• Title: BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion
• Authors: Yonghao Yu, Shunan Zhu, Huai Qin, Haorui Li,
• Abstract summary: BoostDream is a highly efficient plug-and-play 3D refining method designed to transform coarse 3D assets into high-quality. We introduce 3D model distillation that fits differentiable representations from the 3D assets obtained through feed-forward generation. A novel multi-view SDS loss is designed, which utilizes a multi-view aware 2D diffusion model to refine the 3D assets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Witnessing the evolution of text-to-image diffusion models, significant strides have been made in text-to-3D generation. Currently, two primary paradigms dominate the field of text-to-3D: the feed-forward generation solutions, capable of swiftly producing 3D assets but often yielding coarse results, and the Score Distillation Sampling (SDS) based solutions, known for generating high-fidelity 3D assets albeit at a slower pace. The synergistic integration of these methods holds substantial promise for advancing 3D generation techniques. In this paper, we present BoostDream, a highly efficient plug-and-play 3D refining method designed to transform coarse 3D assets into high-quality. The BoostDream framework comprises three distinct processes: (1) We introduce 3D model distillation that fits differentiable representations from the 3D assets obtained through feed-forward generation. (2) A novel multi-view SDS loss is designed, which utilizes a multi-view aware 2D diffusion model to refine the 3D assets. (3) We propose to use prompt and multi-view consistent normal maps as guidance in refinement.Our extensive experiment is conducted on different differentiable 3D representations, revealing that BoostDream excels in generating high-quality 3D assets rapidly, overcoming the Janus problem compared to conventional SDS-based methods. This breakthrough signifies a substantial advancement in both the efficiency and quality of 3D generation processes.

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