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

2026-10-08

AI Summary

This study addresses the limitation of existing motion generation models that overlook the animation principle of exaggeration, resulting in character motions lacking expressiveness. We propose integrating this principle into diffusion- and flow matching-based motion generation frameworks. During training, exaggeration priors are injected via supervised fine-tuning. For inference, we introduce a novel mathematical formulation of Dynamic Movement Primitives (DMPs) to enable training-free exaggeration guidance without retraining. This approach significantly enhances the exaggeration and artistic expressiveness of generated motions while strictly preserving physical plausibility and the original motion intent. Ultimately, this work establishes a new paradigm for generating controllable and dynamically compelling character animations.

Abstract

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 into motion generative pipelines is essential for producing motions that serve not only physically grounded applications but also the needs of the character animation community. This enables the creation of characters that not only move in physically plausible ways but also feel alive, expressive, and engaging. To close this gap, we focus on the Exaggeration principle of animation and investigate how it can be incorporated into modern motion generative pipelines to produce more expressive character motions. To this end, we introduce a framework that operates at two stages of existing motion generative pipelines. The first stage introduces exaggeration during training, where we perform supervised fine-tuning of pre-trained text-to-motion models on our curated exaggeration dataset. The second stage operates at inference time, where we: (i) introduce a mathematical formulation of exaggeration based on dynamic movement primitives (DMPs); and (ii) leverage this formulation as an exaggeration guidance signal to guide existing diffusion and flow-matching text-to-motion generation models toward exaggerated motion without additional training. Through qualitative and quantitative evaluations against three strong motion generation models, we show that our methods generate more exaggerated and expressive motions while preserving neutral reference motion intent and physical plausibility.

Problem

• motion generation
• animation principles
• exaggeration
• character animation
• expressive motion

Innovation

• Controllable Exaggeration
• Dynamic Movement Primitives
• Training-Time Adaptation
• Inference-Time Guidance
• Motion Generation