intuitive physics

IntPhys 2019: A Benchmark for Visual Intuitive Physics Understanding

IntPhys provides a well-designed benchmark for evaluting a system's understanding of a few core concepts about the physics of objects.

Learning Long-term Visual Dynamics with Region Proposal Interaction Networks

Region Proposal Interaction Networks (RPIN) learn to reason about object trajectories in a latent region-proposal feature space, that captures object and contextual information.

Occlusion resistant learning of intuitive physics from videos

Combines a compositional rendering network with a recurrent interaction network to learn dynamics in scenes with significant occlusion, but relies on ground-truth object positions and segmentations.

RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces

RELATE builds upon the interpretable, structured latent parameterization of BlockGAN by modeling the correlations between object parameters to generate realistic videos of dynamic scenes, using raw, unlabeled data.

Intuitive Physics: Current Research and Controversies

Recent research in intuitive physics, guided by knowledge-based and learning-based approaches, shifts to a probabilistic simulation framework that better explains human intuitive physics predictions compared to earlier heuristic models.