DreamTime: An Improved Optimization Strategy for Text-to-3D Content
  Creation
Yukun Huang Jianan Wang Yukai Shi Xianbiao Qi Zheng-Jun Zha Lei Zhang
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.