Paper · arXiv 2306.12422
DreamTime: An Improved Optimization Strategy for Text-to-3D Content Creation
Yukun Huang, Jianan Wang, Yukai Shi, Xianbiao Qi, Zheng-Jun Zha, Lei Zhang
Proposing a redesign in timestep sampling for text-to-3D diffusion models to enhance quality and diversity by aligning with NeRF optimization.
Abstract
Text-to-image diffusion models pre-trained on billions of image-text pairs have recently enabled text-to-3D content creation by optimizing a randomly initialized Neural Radiance Fields (NeRF) with score distillation. However, the resultant 3D models exhibit two limitations: (a) quality concerns such as saturated color and the Janus problem; (b) extremely low diversity comparing to text-guided image synthesis. In this paper, we show that the conflict between NeRF optimization process and uniform timestep sampling in score distillation is the main reason for these limitations. To resolve this conflict, we propose to prioritize timestep sampling with monotonically non-increasing functions, which aligns NeRF optimization with the sampling process of diffusion model. Extensive experiments show that our simple redesign significantly improves text-to-3D content creation with higher quality and diversity.