Controllable Exaggeration for Generative Motion Models via Training-Time Adaptation and Inference-Time Guidance

Recent motion generative models have demonstrated strong capabilities in synthesizing physically plausible character motion, but often overlook established animation principles used by professional animators to ground and design their animation work. Understanding and incorporating these principles