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. Our webpage is available at https://junwuzhang19.github.io/repaint123/.

翻译：近期基于单图生成3D的方法普遍采用评分蒸馏采样（SDS）。尽管取得了令人瞩目的成果，但仍存在多视角不一致、纹理过饱和与过度平滑以及生成速度缓慢等问题。针对这些缺陷，我们提出Repaint123方法，以缓解多视角偏差与纹理退化，并加速生成过程。其核心思想是将2D扩散模型的强大图像生成能力与重绘策略的纹理对齐能力相结合，从而生成具有一致性的高质量多视角图像。我们进一步提出对于重叠区域的可见性感知自适应重绘强度，以提升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采用简单的均方误差（MSE）损失函数即可快速生成3D内容。大量实验表明，我们的方法能够在2分钟内从零开始生成具有多视角一致性与精细纹理的高质量3D内容。相关网页详见https://junwuzhang19.github.io/repaint123/。