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。