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IntPhys: A Framework and Benchmark for Visual Intuitive Physics Reasoning
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In order to reach human performance on complexvisual tasks, artificial systems need to incorporate a sig-nificant amount of understanding of the world in termsof macroscopic objects, movements, forces, etc. Inspiredby work on intuitive physics in infants, we propose anevaluation benchmark which diagnoses how much a givensystem understands about physics by testing whether itcan tell apart well matched videos of possible versusimpossible events constructed with a game engine. Thetest requires systems to compute a physical plausibilityscore over an entire video. It is free of bias and cantest a range of basic physical reasoning concepts. Wethen describe two Deep Neural Networks systems aimedat learning intuitive physics in an unsupervised way,using only physically possible videos. The systems aretrained with a future semantic mask prediction objectiveand tested on the possible versus impossible discrimi-nation task. The analysis of their results compared tohuman data gives novel insights in the potentials andlimitations of next frame prediction architectures.
Forward citations
Cited by 7 Pith papers
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CausalVQA provides 793 paired real-video causal reasoning questions on which the best multimodal model scores 61.66% versus 84.78% for humans, with the largest gaps on anticipation and hypothetical questions.
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IntPhys 2: Benchmarking Intuitive Physics Understanding In Complex Synthetic Environments
IntPhys 2 evaluates models on permanence, immutability, continuity, and solidity in synthetic videos, and finds most models near chance while humans near perfect.
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IMBench: A Benchmark for Intuitive Robotic Manipulation
IMBench is a 35-task robosuite benchmark with a three-stage evaluation showing current VLMs and robot policies fail to convert physical reasoning into executable manipulation.
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SlotPi: Physics-informed Object-centric Reasoning Models
SlotPi combines a learned Hamiltonian energy module with spatiotemporal attention to improve object-centric video prediction and visual question answering on several datasets.
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