arXiv:2509.21592v2 Announce Type: replace-cross Abstract: We consider the problem of forecasting motion from a single image, i.e., predicting how objects in the world are likely to move, without the ability to observe other parameters such as the object velocities or the forces applied to them. We formulate this task as conditional generation of dense trajectory grids with a model that closely follows the architecture of modern video generators but outputs motion trajectories instead of pixels. This approach captures scene-wide dynamics and uncertainty, yielding more accurate and diverse predi
Advances in generative AI models, particularly in video generation, are enabling new applications in motion forecasting, building on existing computer vision capabilities.
Predicting future motion from static images has significant implications for autonomous systems, robotics, and surveillance, improving their ability to anticipate and react to dynamic environments.
The ability to generate dense trajectory grids from single images creates a more robust and nuanced understanding of potential future states, moving beyond simple object detection to predictive dynamics.
- · Autonomous vehicle developers
- · Robotics companies
- · Surveillance technology providers
- · AI research institutions
- · Systems reliant on purely reactive sensing
- · Simpler motion forecasting methods
Improved safety and efficiency in autonomous systems through predictive capabilities.
Accelerated development of general-purpose AI agents capable of understanding and interacting with dynamic physical environments.
Ethical and regulatory considerations around the deployment of highly predictive surveillance systems become more pressing.
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Read at arXiv cs.LG