2019/05/26 by Sébastien Ehrhardt, Aron Monszpart, Ehrhardt, Sébastien +6 · 1 citation
Computer Science · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Graphics (cs.GR) #Human Pose and Action Recognition #cs.AI #cs.CV #cs.GR
paper · pdf · doi:10.48550/arxiv.1905.10793
Under review
arxiv created 2019/05/26 · openalex publication_date 2019/05/26 · arxiv updated 2019/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We are interested in learning models of intuitive physics similar to the ones that animals use for navigation, manipulation and planning. In addition to learning general physical principles, however, we are also interested in learning ``on the fly'', from a few experiences, physical properties specific to new environments. We do all this in an unsupervised manner, using a meta-learning formulation where the goal is to predict videos containing demonstrations of physical phenomena, such as objects moving and colliding with a complex background. We introduce the idea of summarizing past experiences in a very compact manner, in our case using dynamic images, and show that this can be used to solve the problem well and efficiently. Empirically, we show via extensive experiments and ablation studies, that our model learns to perform physical predictions that generalize well in time and space, as well as to a variable number of interacting physical objects.