InterDyn: Controllable Interactive Dynamics with Video Diffusion Models

• URL: http://arxiv.org/abs/2412.11785v1
• Date: Mon, 16 Dec 2024 13:57:02 GMT
• Title: InterDyn: Controllable Interactive Dynamics with Video Diffusion Models
• Authors: Rick Akkerman, Haiwen Feng, Michael J. Black, Dimitrios Tzionas, Victoria Fernández Abrevaya,
• Abstract summary: We propose InterDyn, a framework that generates videos of interactive dynamics given an initial frame and a control signal encoding the motion of a driving object or actor. Our key insight is that large video foundation models can act as both neurals and implicit physics simulators by learning interactive dynamics from large-scale video data.
• Score: 50.38647583839384
• License:
• Abstract: Predicting the dynamics of interacting objects is essential for both humans and intelligent systems. However, existing approaches are limited to simplified, toy settings and lack generalizability to complex, real-world environments. Recent advances in generative models have enabled the prediction of state transitions based on interventions, but focus on generating a single future state which neglects the continuous motion and subsequent dynamics resulting from the interaction. To address this gap, we propose InterDyn, a novel framework that generates videos of interactive dynamics given an initial frame and a control signal encoding the motion of a driving object or actor. Our key insight is that large video foundation models can act as both neural renderers and implicit physics simulators by learning interactive dynamics from large-scale video data. To effectively harness this capability, we introduce an interactive control mechanism that conditions the video generation process on the motion of the driving entity. Qualitative results demonstrate that InterDyn generates plausible, temporally consistent videos of complex object interactions while generalizing to unseen objects. Quantitative evaluations show that InterDyn outperforms baselines that focus on static state transitions. This work highlights the potential of leveraging video generative models as implicit physics engines.

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