title: DreamTime: An Improved Optimization Strategy for Text-to-3D Content
    Creation
summary: 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.
paper_authors._id: 6493c7d774db6cc30f41a594
paper_authors.name: Yukun Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-23T07:31:55.127Z
paper_authors._id: 638ee900ee7e45e0474a5712
paper_authors.avatarUrl: /avatars/eadb5ae2fc92bd9af5516acbc8f1bdf0.svg
paper_authors.fullname: Yukun Huang
paper_authors.type: user
paper_authors.user: KevinHuang
paper_authors._id: 6493c7d774db6cc30f41a595
paper_authors.name: Jianan Wang
paper_authors._id: 6493c7d774db6cc30f41a596
paper_authors.name: Yukai Shi
paper_authors._id: 6493c7d774db6cc30f41a597
paper_authors.name: Xianbiao Qi
paper_authors._id: 6493c7d774db6cc30f41a598
paper_authors.name: Zheng-Jun Zha
paper_authors._id: 6493c7d774db6cc30f41a599
paper_authors.name: Lei Zhang
paper_title: DreamTime: An Improved Optimization Strategy for Text-to-3D Content
    Creation
paper_summary: 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.
paper_ai_keywords: text-to-image diffusion models Neural Radiance Fields (NeRF) score distillation saturated color Janus problem timestep sampling monotonically non-increasing functions diffusion model text-guided image synthesis