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Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments
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Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments
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Embodied systems experience the world as 'a symphony of flows': a combination of many continuous streams of sensory input coupled to self-motion, interwoven with the dynamics of external objects. These sensory streams and the underlying dynamics of the world obey smooth, time-parameterized symmetries which existing world models ignore. Without a memory that respects this structure, partial observability presents a major obstacle to existing methods: each observation reveals only a fraction of the world, while unobserved regions continue to evolve. In this work, we introduce Flow Equivariant World Modeling, a framework that leverages time-parameterized symmetries within a latent memory for stable and accurate dynamics prediction over long horizons. The latent memory shifts and transforms equivariantly with self-motion and inferred external object motion, keeping information about out-of-view regions aligned as time progresses. We demonstrate the advantage of this framework over state-of-the-art diffusion, memory-augmented, and recurrent world model architectures on 2D and 3D partially observed video world modeling benchmarks. More broadly, our results suggest that predictive representations become more powerful when they are organized in line with the temporal and dynamical structure of the world they model. Project page: https://flowequivariantworldmodels.github.io/
Forward citations
Cited by 6 Pith papers
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MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
MemoBench curates 360 ground-truth clips and an evaluation suite to diagnose memory consistency failures in video models when objects change state while out of view.
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MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
MemoBench is a new diagnostic benchmark with 360 synthetic and real clips plus VQA evaluation that tests memory consistency in video models under the disappear-and-reappear paradigm in dynamically changing environments.
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MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
MemoBench is a new diagnostic benchmark with automated and VQA metrics that evaluates memory consistency in video models under disappear-and-reappear in dynamic environments.
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MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
Current video world models do not reliably recover an object's updated state after it disappears and reappears under simultaneous camera and scene dynamics.
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MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
MemoBench curates 360 clips and an evaluation suite to test video models on recovering updated object states after disappear-and-reappear in changing environments.
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Echo-Memory: A Controlled Study of Memory in Action World Models
A controlled study finds that block-wise state-space recurrence outperforms other memory designs for open-domain scene return in action-conditioned video models, and that standard replay metrics do not adequately meas...
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