The efficiency of adapting pre-trained motion modules to new motion patterns is crucial, especially for users wanting specific effects without incurring heavy pre-training costs.
MotionLoRA employs efficient fine-tuning techniques on limited reference videos, enabling rapid adaptation of motion patterns like zooming and panning.
Adding LoRA layers to the self-attention components of the motion module allows it to be trained on new motion types with fewer resources.
The approach in AnimateDiff not only handles general motion priors but also specializes in personalizing motion generation based on user-specific needs.
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