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Updated: May 14, 2025

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

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MotionCrafter: Plug-and-Play Motion Guidance for Diffusion Models.

Yuxin Zhang, Weiming Dong, Fan Tang

IEEE Transactions on Visualization and Computer Graphics

|May 12, 2025

Summary

This summary is machine-generated.

MotionCrafter enables precise video motion customization using reference videos for diffusion models. This method effectively separates appearance and motion, allowing for dynamic content generation with style control.

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Text-to-video diffusion models excel at content diversity but struggle with specific motion control.
Controlling character actions, object movements, and camera motion requires more precise guidance than text prompts alone offer.

Purpose of the Study:

To introduce MotionCrafter, a novel one-shot instance-guided method for customizing video motion.
To effectively decouple appearance and motion within video clips for enhanced control.
To enable precise motion guidance using user-specified reference videos.

Main Methods:

MotionCrafter utilizes a parallel spatial-temporal architecture integrating reference motion into the temporal component.
A dual-branch motion disentanglement approach with a specific loss and appearance prior enhancement is proposed.
A timestep-layered tuning strategy focuses the diffusion model on motion-level information.

Main Results:

MotionCrafter successfully integrates dynamic motions while preserving the base model's coherence and quality.
The method allows for a wide range of appearance generation capabilities and style control.
Experiments demonstrate effective motion customization and user preference for generated videos.

Conclusions:

MotionCrafter provides a robust solution for motion customization in text-to-video and text-to-image diffusion models.
The approach facilitates the generation of videos with specific dynamic motions and diverse artistic styles.
This method enhances the practical application of generative models for personalized video creation.