MotionFollower: Transfer Motion from One Video Character to Another 189

In the dynamic realm of video editing, the ability to seamlessly transfer motion from one video to another has long been a coveted feature. MotionFollower emerges as a groundbreaking solution, enabling precise motion editing while preserving the original video's background and the appearance of its main subject. This lightweight, score-guided diffusion model redefines what's possible in video motion editing, offering unparalleled performance and efficiency.

For example, suppose we have two videos:

Source video: A scenic park video where the main subject is a woman wearing red clothing, standing still with no motion.

Target video: A dance video where the main subject is a man wearing blue clothing, dancing.

Using MotionFollower, we can transfer the dancing motion of the man in the target video to the woman in the source video.

At the same time, while transferring the motion information to the subject in the source video, the appearance of the subject in the source video is preserved—including their clothing, facial features, and other characteristics—only their actions are changed.

Technical Insights:

MotionFollower stands out by addressing the challenge of modifying motion information in a video without compromising the appearance of the original subject and the background. MotionFollower leverages lightweight signal controllers to manage pose and appearance, integrating these controls into the denoising process through convolutional blocks, thereby eliminating the need for extensive attention computations. The model employs a two-branch architecture with reconstruction and editing branches, guided by score-guided principles to ensure the preservation of texture details and complex backgrounds.

Architecture overview. During training, the two lightweight signal controllers and the U-Net are trainable.

At the core of MotionFollower is the ability to manipulate motion by encoding the target pose and source appearance as discrete signals. The score-guided principle enhances modeling capability by injecting appropriate guidance during the score estimation process to maintain consistency between the original and edited videos. This approach significantly reduces GPU memory usage (up to 80% less compared to leading competitors such as MotionEditor) while delivering superior performance in handling large camera movements and complex backgrounds.

Qualitative comparison of our MotionFollower with other state-of-the-art models.

Industry Applications:

The applications of MotionFollower span across various industries. In entertainment, it enables editors to transfer complex dance moves or action sequences from reference videos to new content, revolutionizing video production by creating stunning visual effects without the need for extensive reshoots. In advertising, brands can craft dynamic ads where products interact with ever-changing environments, boosting audience engagement and creativity. Furthermore, in the field of sports analysis, MotionFollower can be used to analyze athletes' movements, helping coaches and trainers develop better training programs through visualization and comparison of techniques.

Future Outlook:

Looking ahead, MotionFollower lays the groundwork for further advancements in video editing technology. Future developments may include real-time motion editing capabilities, expanding its use in live streaming and interactive virtual reality experiences. The integration of AI-driven enhancements will continue to push boundaries, allowing for more precise and creative control over video content. As the technology matures, it will usher in a new era of video production, where the only limit is imagination.

MotionFollower is more than just a tool; it is a catalyst for innovation in video editing. By offering a lightweight, efficient, and powerful motion transfer solution, it opens up new possibilities for creators across all fields. This revolutionary model sets a new standard for video motion editing, making complex, high-quality editing accessible to all.