Free-T2M：基于频域的人形机器人鲁棒文本到动作生成
Free-T2M: Robust Text-to-Motion Generation for Humanoid Robots via Frequency-Domain
AI总结：
本文提出Free-T2M框架，从频域视角将文本到动作生成分解为低频语义规划与高频细节执行两个阶段，并通过阶段特定的频域一致性对齐和频域时间自适应模块提升运动质量与语义正确性，在StableMoFusion上FID从0.152降至0.060，达到扩散架构新最优。
摘要：
使人形机器人能够根据自然语言指令合成复杂且物理连贯的动作，是自主机器人与人机交互的基石。尽管扩散模型在这一文本到动作（T2M）任务中展现出潜力，但它们通常生成语义有缺陷或不稳定的动作，限制了其在真实机器人上的适用性。本文从频域视角重新审视T2M问题，揭示生成过程类似于一种分层控制范式。我们识别出两个关键阶段：语义规划阶段，其中低频分量建立全局运动轨迹；以及细粒度执行阶段，其中高频细节细化运动。为应对每个阶段的不同挑战，我们提出了频率增强的文本到动作框架Free-T2M，该框架融合了阶段特定的频域一致性对齐。我们设计了一个频域时间自适应模块，以调制不同频段的对齐效果。这些设计增强了基础语义规划的鲁棒性，并提高了细节执行的准确性。大量实验表明，我们的方法显著提升了运动质量和语义正确性。值得注意的是，当应用于StableMoFusion基线时，Free-T2M将FID从0.152降低至0.060，在扩散架构中建立了新的最优水平。这些发现强调了频域洞察对于生成鲁棒且可靠动作的关键作用，为更直观的机器人自然语言控制铺平了道路。
英文摘要：
Enabling humanoid robots to synthesize complex, physically coherent motions from natural language commands is a cornerstone of autonomous robotics and human-robot interaction. While diffusion models have shown promise in this text-to-motion (T2M) task, they often generate semantically flawed or unstable motions, limiting their applicability to real-world robots. This paper reframes the T2M problem from a frequency-domain perspective, revealing that the generative process mirrors a hierarchical control paradigm. We identify two critical phases: a semantic planning stage, where low-frequency components establish the global motion trajectory, and a fine-grained execution stage, where high-frequency details refine the movement. To address the distinct challenges of each phase, we introduce Frequency enhanced text-to-motion (Free-T2M), a framework incorporating stage-specific frequency-domain consistency alignment. We design a frequency-domain temporal-adaptive module to modulate the alignment effects of different frequency bands. These designs enforce robustness in the foundational semantic plan and enhance the accuracy of detailed execution. Extensive experiments show our method dramatically improves motion quality and semantic correctness. Notably, when applied to the StableMoFusion baseline, Free-T2M reduces the FID from 0.152 to 0.060, establishing a new state-of-the-art within diffusion architectures. These findings underscore the critical role of frequency-domain insights for generating robust and reliable motions, paving the way for more intuitive natural language control of robots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
