arXiv:2605.24566v1 Announce Type: cross Abstract: 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 We
This development addresses a critical limitation in current human motion diffusion models, moving beyond ambiguous text prompts to more precise, quantitative control over synthesized movement intensity, aligning with the ongoing push for more nuanced AI capabilities.
Precise control over motion intensity in human motion diffusion is crucial for applications ranging from realistic virtual agents and robotics to advanced animation, enabling more sophisticated and expressive AI-generated motor skills.
The ability to condition diffusion models on numerical effort signals, rather than vague adverbs, allows for the generation of more dynamic, nuanced, and quantitatively controlled human motion, enhancing realism and utility.
- · AI developers
- · Robotics companies
- · Animation studios
- · Virtual reality platforms
- · Platforms reliant on generic motion assets
- · AI models without fine-grained control
Human motion diffusion models gain significantly improved control over synthesized movement dynamics.
This leads to more lifelike virtual characters, advanced humanoid robot programming, and highly customized digital human avatars.
The enhanced realism and control could accelerate the adoption of digital humans in diverse sectors, blurring the lines between real and simulated interactions.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG