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PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation

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arxiv 2404.13026 v2 pith:FIOBFFEI submitted 2024-04-19 cs.CV cs.AI

classification cs.CVcs.AI
keywords objectsdynamicsobjectphysdreamerinteractionspropertiesrealisticgeneration
verification ladder T0 review T1 audit T2 compute T3 formal
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Realistic object interactions are crucial for creating immersive virtual experiences, yet synthesizing realistic 3D object dynamics in response to novel interactions remains a significant challenge. Unlike unconditional or text-conditioned dynamics generation, action-conditioned dynamics requires perceiving the physical material properties of objects and grounding the 3D motion prediction on these properties, such as object stiffness. However, estimating physical material properties is an open problem due to the lack of material ground-truth data, as measuring these properties for real objects is highly difficult. We present PhysDreamer, a physics-based approach that endows static 3D objects with interactive dynamics by leveraging the object dynamics priors learned by video generation models. By distilling these priors, PhysDreamer enables the synthesis of realistic object responses to novel interactions, such as external forces or agent manipulations. We demonstrate our approach on diverse examples of elastic objects and evaluate the realism of the synthesized interactions through a user study. PhysDreamer takes a step towards more engaging and realistic virtual experiences by enabling static 3D objects to dynamically respond to interactive stimuli in a physically plausible manner. See our project page at https://physdreamer.github.io/.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Hallo4D uses vision-language models to detect and correct spatial and temporal mistakes in AI-generated 3D and 4D content, improving consistency without retraining the base generators.

  2. TRACE: Learning 3D Gaussian Physical Dynamics from Multi-view Videos

    cs.CV 2025-08 conditional novelty 6.0 of 10

    TRACE predicts future frames of dynamic 3D scenes by learning a per-particle translation-rotation dynamics system inside 3D Gaussian Splatting, without labels.

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