Repaint123: Fast and High-quality One Image to 3D Generation with
  Progressive Controllable 2D Repainting
Authors: name: Junwu Zhang, name: Zhenyu Tang, name: Yatian Pang, name: Xinhua Cheng, name: Peng Jin, name: Yida Wei, name: Wangbo Yu, name: Munan Ning, name: Li Yuan

Recent one image to 3D generation methods commonly adopt Score Distillation
Sampling (SDS). Despite the impressive results, there are multiple deficiencies
including multi-view inconsistency, over-saturated and over-smoothed textures,
as well as the slow generation speed. To address these deficiencies, we present
Repaint123 to alleviate multi-view bias as well as texture degradation and
speed up the generation process. The core idea is to combine the powerful image
generation capability of the 2D diffusion model and the texture alignment
ability of the repainting strategy for generating high-quality multi-view
images with consistency. We further propose visibility-aware adaptive
repainting strength for overlap regions to enhance the generated image quality
in the repainting process. The generated high-quality and multi-view consistent
images enable the use of simple Mean Square Error (MSE) loss for fast 3D
content generation. We conduct extensive experiments and show that our method
has a superior ability to generate high-quality 3D content with multi-view
consistency and fine textures in 2 minutes from scratch. Code is at
https://github.com/junwuzhang19/repaint123.