通过目标预测提升3D人体动作生成的流畅性与精度
FlowMotion: Target-Predictive Conditional Flow Matching for Jitter-Reduced Text-Driven Human Motion Generation
- 基于条件流匹配，聚焦目标动作精准预测
- 在KIT和HumanML3D数据集上实现最优/次优抖动性能
- 兼顾生成质量与时间自然性，适合实时应用
高保真且时间平滑的3D人体动作生成仍是挑战，尤其在资源受限环境下。本文提出FlowMotion，一种基于条件流匹配（CFM）的新方法。其训练目标专注于更准确地预测3D人体动作的目标状态，显著提升生成保真度与时间连续性，同时保持流匹配方法固有的快速合成特性。FlowMotion在KIT数据集上达到最优抖动表现，在HumanML3D数据集上位列第二，且在两个数据集上均取得具有竞争力的FID值。该方法生成的运动序列更具鲁棒性与自然感，实现了生成质量与时间自然性的良好平衡。
原文摘要 · Abstract (English)
Achieving high-fidelity and temporally smooth 3D human motion generation remains a challenge, particularly within resource-constrained environments. We introduce FlowMotion, a novel method leveraging Conditional Flow Matching (CFM). FlowMotion incorporates a training objective within CFM that focuses on more accurately predicting target motion in 3D human motion generation, resulting in enhanced generation fidelity and temporal smoothness while maintaining the fast synthesis times characteristic of flow-matching-based methods. FlowMotion achieves state-of-the-art jitter performance, achieving the best jitter in the KIT dataset and the second-best jitter in the HumanML3D dataset, and a competitive FID value in both datasets. This combination provides robust and natural motion sequences, offering a promising equilibrium between generation quality and temporal naturalness.
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