用分阶段一致性模型实现每秒30帧的实时人体动作生成
MotionPCM: Real-Time Motion Synthesis with Phased Consistency Model
- 采用分阶段一致性模型，单步采样即可生成动作
- 在HumanML3D上达38.9%的FID提升，速度超30帧/秒
- 适合需要低延迟动作生成的应用，如游戏与虚拟现实
扩散模型因其强大的生成能力成为人体动作合成的热门选择，但其高计算复杂度和大量采样步骤限制了实时应用。一致性模型（CM）可将采样步数从数百减少至几近四步，显著加速生成过程。然而，将CM应用于潜空间中的文本条件人体动作合成时，生成质量不佳。本文提出MotionPCM，一种基于分阶段一致性模型的方法，旨在提升潜空间中实时动作合成的质量与效率。在HumanML3D数据集上的实验表明，该模型可在单次采样下实现超过30帧/秒的实时推理，同时在FID指标上相比先前最优方法提升38.9%。代码将公开以供复现。
原文摘要 · Abstract (English)
Diffusion models have become a popular choice for human motion synthesis due to their powerful generative capabilities. However, their high computational complexity and large sampling steps pose challenges for real-time applications. Fortunately, the Consistency Model (CM) provides a solution to greatly reduce the number of sampling steps from hundreds to a few, typically fewer than four, significantly accelerating the synthesis of diffusion models. However, applying CM to text-conditioned human motion synthesis in latent space yields unsatisfactory generation results. In this paper, we introduce \textbf{MotionPCM}, a phased consistency model-based approach designed to improve the quality and efficiency for real-time motion synthesis in latent space. Experimental results on the HumanML3D dataset show that our model achieves real-time inference at over 30 frames per second in a single sampling step while outperforming the previous state-of-the-art with a 38.9\% improvement in FID. The code will be available for reproduction.
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