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
this https URL

将图像生成、三维、多视角一致性、纹理退化和重新绘制策略作为主要研究区域，提出一种能够快速生成高质量三维内容的方法。

Repaint123：高质量的快速一图生成 3D 技术，具有渐进可控 2D 再绘制

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

Co-speech gesture generation is crucial for automatic digital avatar
animation. However, existing methods suffer from issues such as unstable
training and temporal inconsistency, particularly in generating high-fidelity
and comprehensive gestures. Additionally, these methods lack effective control
over speaker identity and temporal editing of the generated gestures. Focusing
on capturing temporal latent information and applying practical controlling, we
propose a Controllable Co-speech Gesture Generation framework, named C2G2.
Specifically, we propose a two-stage temporal dependency enhancement strategy
motivated by latent diffusion models. We further introduce two key features to
C2G2, namely a speaker-specific decoder to generate speaker-related real-length
skeletons and a repainting strategy for flexible gesture generation/editing.
Extensive experiments on benchmark gesture datasets verify the effectiveness of
our proposed C2G2 compared with several state-of-the-art baselines. The link of
the project demo page can be found at this https URL

我们提出了一种名为 C2G2 的可控共语手势生成框架，该框架通过捕捉时间潜变信息和应用实用的控制方法来解决现有方法中存在的训练不稳定、时间不一致、高保真度和全面性不足以及对生成手势的说话者身份和时间编辑的缺乏有效控制等问题，通过两阶段的时间依赖增强策略和说话者特定解码器生成与说话者相关的真实长度骨骼以及柔性手势生成 / 编辑的重新上色策略，我们在基准手势数据库上进行了大量实验，验证了我们提出的 C2G2 相对于几种最先进的基准效果。