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.

翻译：摘要：近期单图像三维生成方法普遍采用分数蒸馏采样技术。尽管效果显著，但存在多视图不一致、纹理过饱和与过度平滑，以及生成速度缓慢等缺陷。针对这些问题，我们提出Repaint123方法，以缓解多视图偏差与纹理退化，并加速生成过程。其核心思想是结合二维扩散模型的强大图像生成能力与重绘策略的纹理对齐特性，生成具有一致性的高质量多视角图像。我们进一步提出可见性自适应重绘强度策略，用于处理重叠区域，从而提升重绘过程中的生成图像质量。生成的高质量且多视图一致的图像，使我们能够采用简单的均方误差损失函数实现快速三维内容生成。大量实验表明，本方法具备卓越能力，可在2分钟内从零开始生成具有多视图一致性与精细纹理的高质量三维内容。代码开源地址：https://github.com/junwuzhang19/repaint123