Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting

Junwu Zhang, Zhenyu Tang, Yatian Pang, Xinhua Cheng, Peng Jin, Yida Wei, Wangbo Yu, Munan Ning, yl2488

Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting: 4 upvotes on Hugging Face Daily Papers, #14 of 15 papers on 2023-12-21. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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