Text to video models can unlearn specific motions without retraining
Motion Concept Unlearning in Video Diffusion Models
Computer Vision and Pattern Recognition
Summary
Generating videos with realistic actions like kicking or shooting can create safety concerns. The authors studied how to erase specific motion concepts from text-to-video AI models without retraining the entire system. They found that their method, called MUTE, can selectively remove targeted motions while keeping the video looking natural, and it works across different video generation models. This approach helps control which motions a model can produce, improving safety and customization.
What this means in practice
- •For video generation developers: Remove unwanted motion features from generated videos to improve content safety and meet ethical guidelines without retraining the model.
- •For digital content moderation teams: Filter or suppress specific motion types in AI-generated video content to comply with platform policies on violent or harmful actions.
Authors
Ping Liu, Chi Zhang
Abstract
Text-to-video (T2V) diffusion models can generate realistic depictions of actions such as kicking, stabbing, and shooting, raising safety concerns that motivate targeted concept erasure. Although concept erasure has been extensively studied for static concepts in text-to-image and T2V models, erasing motion concepts remains largely unexplored. We present a systematic study of motion concept erasure in video Diffusion Transformers (DiTs). Through causal interventions, we show that text-conditioning attention carries concept-specific motion information and supports selective intervention, whereas perturbing temporal positional encoding suppresses both target and non-target dynamics. We further find that directly adapting ESD, a representative weight-level image erasure method, to a video DiT yields modest and uneven motion suppression: reducing its erasure training loss does not by itself remove the concept signal from the difference between the conditional and unconditional predictions, which classifier-free guidance (CFG) then scales at every denoising step. From these findings, we derive three requirements for motion concept erasure: concept specificity, spatial selectivity, and temporal naturalness. Each determines one component of MUTE (Motion concept Unlearning in Text-to-video gEneration): at each denoising step, MUTE extracts a concept direction through token neutralization, derives a spatial gate from the direction's intrinsic structure, and subtracts the resulting correction from the velocity output before CFG is applied. MUTE is training-free and requires no weight modification. Experiments on 20 motion concepts show that MUTE outperforms representative prompt-level, weight-level, and inference-time baselines on Wan2.1-T2V, and the same formulation transfers to CogVideoX, supporting its applicability across distinct T2V attention architectures.