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

arXiv:2610.12316 · cs.CV · Submitted 2026-10-08 · Read on arXiv

cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Project page: https://ahhhz975.github.io/ControllableMotionExaggeration

Terminology

• Fine-Tuning — 46× in this paper · explained in 5 episodes
• Prompt Adherence — 23× in this paper · explained in 2 episodes
• Ablation — 20× in this paper · explained in 1 episode
• Fidelity — 19× in this paper · explained in 2 episodes
• Foundation Model — 10× in this paper · explained in 3 episodes
• Foundation Models — 10× in this paper · explained in 7 episodes
• Generative Model — 9× in this paper · explained in 2 episodes
• Adaptation — 8× in this paper · explained in 1 episode
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Generative Models — 7× in this paper · explained in 2 episodes
• Ablation study — 6× in this paper · explained in 1 episode
• Transformer — 6× in this paper · explained in 2 episodes
• Data Augmentation — 5× in this paper · explained in 7 episodes
• Ablation Studies — 4× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Diffusion Process — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• LoRA Adapters — 3× in this paper · explained in 3 episodes
• Supervised Fine-Tuning (SFT) — 3× in this paper · explained in 8 episodes
• ACM Transactions on Graphics — 2× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Catastrophic Forgetting — 2× in this paper · explained in 5 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Frozen model — 2× in this paper · explained in 3 episodes
• Iterative Refinement — 2× in this paper · explained in 3 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Affective Computing — 1× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Flow Matching Model — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
• LoRA: Low-Rank Adaptation of Large Language Models
• Motion Generation: A Survey of Generative Approaches and Benchmarks
• Adam: A Method for Stochastic Optimization
• Decoupled Weight Decay Regularization
• Kimodo: Scaling Controllable Human Motion Generation
• Denoising Diffusion Implicit Models
• HY-Motion 1.0: Scaling Flow Matching Models for Text-To-Motion Generation
• AnimationBench: Are Video Models Good at Character-Centric Animation?
• Qwen3 Technical Report

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