

Motionfollower
Overview :
MotionFollower is a lightweight score-guided diffusion model designed for video motion editing. It utilizes two lightweight signal controllers to separately control pose and appearance, avoiding heavy attention computations. The model employs a dual-branch architectural design based on the score-guided principle, including reconstruction and editing branches, significantly enhancing its ability to model texture details and complex backgrounds. Experiments demonstrate that MotionFollower reduces GPU memory usage by approximately 80% compared to the state-of-the-art motion editing model MotionEditor, while providing superior motion editing performance. It also exclusively supports extensive camera movement and complex actions.
Target Users :
MotionFollower is suitable for professional video editors and researchers who require high-quality video motion editing, especially those who need to fine-tune video movement while maintaining the original character appearance and background. Its lightweight nature and efficient memory usage make it an ideal choice for resource-constrained environments.
Use Cases
In film post-production, use MotionFollower to fine-tune action scenes.
VR content creators utilize this model to achieve realistic simulations of complex actions.
Researchers in video analysis and motion capture research use this model for data augmentation.
Features
Lightweight score-guided diffusion model, optimized for video motion editing.
Utilizes lightweight signal controllers to simplify the control process for pose and appearance.
Dual-branch architecture design enhances the modeling of texture details and complex backgrounds.
Consistency regularization and loss function ensure the consistency of model output.
Significantly reduces GPU memory usage, improving computational efficiency.
Supports editing of extensive camera movement and complex actions.
How to Use
1. Visit the MotionFollower GitHub page to learn about the model's basic information and features.
2. Read the README file for installation and usage instructions for the model.
3. Install the necessary dependencies according to the guide and configure the runtime environment.
4. Download and load the model, preparing the input data required for video editing.
5. Set the model parameters, such as pose and appearance controllers, according to specific needs.
6. Run the model to observe the video motion editing results and make necessary adjustments.
7. Save the edited video and perform further post-processing as needed.
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