CVPR 2025 Insights #3: Learning from Movement. Paper Presented at CVPR 2025 Oral Session | Poster #173
Recent advances in video generation have introduced methods for text-to-video and region-specific control. But what if we could condition a video model on any motion using a unified, intuitive representation?
Motion Prompting does precisely that. This work introduces a method to control AI-generated videos using point trajectories, user-defined paths over space and time, enabling general and flexible motion conditioning.
Method Overview
- The team fine-tunes Lumiere, a base video diffusion model, with a ControlNet that accepts a rasterized space-time volume of point tracks.
- Tracks are extracted using video tracking algorithms and embedded with 64D vectors acting as unique identifiers.
- It supports arbitrary density, duration, and location of motion signals, which are far more general than bounding boxes or sparse keypoints.
Key Applications
- Interactive Video Editing: Click-and-drag input turns still images into dynamic videos with localized, consistent motion.
- Camera & Object Control: Depth-based point clouds allow synthetic camera movement (e.g., dolly zooms).
- Motion Transfer: Animate new images with motion from a reference video.
- Motion Magnification: Subtle motions like breathing are amplified by scaling point trajectories.
Limitations
- Bidirectional generation leads to non-causal effects (e.g., motion anticipation).
- Ambiguities in overlapping motion regions may produce unintended results.
- Requires ~10 minutes per video; real-time interaction is still under exploration.
Relevance to FiftyOne
While Motion Prompting does not directly intersect with FiftyOne, there are clear synergy points:
- Point trajectory data (input/output) could be analyzed, visualized, or labeled using FiftyOne’s spatial-temporal tools.
- Motion transfer outputs might benefit from frame-by-frame evaluation, error diagnosis, or comparative visualization.
- Future work could integrate trajectory-based interaction logs into FiftyOne for dataset curation or model debugging.
Conclusion
“Motion Prompting” introduces a scalable, general-purpose way to control video synthesis via point tracks, unlocking a wide range of editing and interactive generation capabilities. It’s a valuable contribution for researchers in video synthesis, human-computer interaction, and creative AI.
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