Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting
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.
最近的一种从单张图片生成三维内容的方法通常采用分数蒸馏采样（SDS）。尽管结果令人印象深刻，但还存在多个缺陷，包括多视角不一致性、过饱和和过平滑的纹理，以及生成速度慢。为了解决这些缺陷，我们提出了 Repaint123，旨在减轻多视角偏见以及纹理退化，并加快生成过程。核心思想是结合二维扩散模型的强大图像生成能力和重绘策略的纹理对齐能力，生成具有一致性的高质量多视角图像。我们进一步提出了可见性感知的自适应重绘强度，以增强重绘过程中生成图像的质量。生成的高质量且多视角一致的图像使得使用简单的均方误差（MSE）损失快速生成三维内容成为可能。我们进行了广泛的实验，并展示了我们的方法在2分钟内从零开始生成具有多视角一致性和精细纹理的高质量三维内容的卓越能力。