论文标题
降级扩散概率模型，用于造型步行合成
Denoising Diffusion Probabilistic Models for Styled Walking Synthesis
论文作者
论文摘要
为数字人类生成现实动作是为许多图形应用程序耗时的。数据驱动的运动合成方法近年来通过深层生成模型取得了稳定的进步。这些结果提供了高质量的动作，但通常会遭受运动风格多样性的影响。我们首次提出了一个使用denoisis扩散概率模型（DDPM）的框架，以合成样式的人类运动，将两个任务集成到一个管道中，与传统运动合成方法相比，风格多样性增加。实验结果表明，我们的系统可以产生高质量和多样化的步行动作。
Generating realistic motions for digital humans is time-consuming for many graphics applications. Data-driven motion synthesis approaches have seen solid progress in recent years through deep generative models. These results offer high-quality motions but typically suffer in motion style diversity. For the first time, we propose a framework using the denoising diffusion probabilistic model (DDPM) to synthesize styled human motions, integrating two tasks into one pipeline with increased style diversity compared with traditional motion synthesis methods. Experimental results show that our system can generate high-quality and diverse walking motions.
