EMA: Effort Metric Attention for Anatomical Effort-Guided Human Motion Diffusion
For researchers in human motion generation, this provides a method for fine-grained, interpretable control of motion intensity without post-hoc optimization.
The paper tackles the challenge of controlling motion intensity in human motion diffusion models. They propose Effort Metric Attention (EMA), a cross-attention module conditioning on numerical effort signals, achieving near-monotonic alignment between specified effort levels and generated motion dynamics.
Human motion diffusion models can synthesize action sequences from text, but controlling motion intensity remains challenging. Existing approaches rely on effort-related adverbs, which are ambiguous and fail to capture quantitative aspects such as pacing, often resulting in flat and monotonous dynamics. We propose an intensity-control framework based on Effort Metric Attention (EMA), a cross-attention module that conditions diffusion on numerical effort signals. Inspired by Laban Movement Analysis (LMA), the framework focuses on the Time and Weight effort factors. We approximate these factors using two kinematic metrics: peak joint positional change for pacing and collective joint positional change for motion amount. EMA enables fine-grained, region-wise control without costly post-hoc optimization. We introduce two evaluation tasks, metric-to-motion consistency and body-part-level effort modulation, to assess numerical fidelity and localized control. Experiments and a user study show near-monotonic alignment between specified effort levels, generated motion dynamics, and established LMA descriptors. These results indicate effective and interpretable control of effort dynamics in practice.