Schrödinger's Cat: Probabilistic Representation and Prediction of Potential Scene Kinematics
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TL;DR - GARFIELD models a distribution of possible future scene motions from an image and optional sparse constraints, rather than predicting one trajectory. It supports fast, uncertainty-aware motion planning and interactive refinement.
- Uses a structured spatiotemporal latent representation to jointly sample scene trajectories.
- A deterministic density decoder localizes motion uncertainty by scene element and timestep.
- Additional constraints progressively refine the predicted future-motion distribution.
- Achieves competitive planning performance while sampling trajectories 97× faster than large video-generation models and estimating densities roughly 100× faster than Monte Carlo sampling.
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Schrödinger's Cat: Probabilistic Representation and Prediction of Potential Scene Kinematics
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Hugging Face upvotes 0
TL;DR - GARFIELD models a distribution of possible future scene motions from an image and optional sparse constraints, rather than predicting one trajectory. It supports fast, uncertainty-aware motion planning and interactive refinement.
- Uses a structured spatiotemporal latent representation to jointly sample scene trajectories.
- A deterministic density decoder localizes motion uncertainty by scene element and timestep.
- Additional constraints progressively refine the predicted future-motion distribution.
- Achieves competitive planning performance while sampling trajectories 97× faster than large video-generation models and estimating densities roughly 100× faster than Monte Carlo sampling.