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PhysGen3D: Crafting a Miniature Interactive World from a Single Image

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arxiv 2503.20746 v1 pith:TKAURZ2U submitted 2025-03-26 cs.CV

PhysGen3D: Crafting a Miniature Interactive World from a Single Image

classification cs.CV
keywords physgen3dimageinteractivephysicalsingleworldcontrolframework
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Envisioning physically plausible outcomes from a single image requires a deep understanding of the world's dynamics. To address this, we introduce PhysGen3D, a novel framework that transforms a single image into an amodal, camera-centric, interactive 3D scene. By combining advanced image-based geometric and semantic understanding with physics-based simulation, PhysGen3D creates an interactive 3D world from a static image, enabling us to "imagine" and simulate future scenarios based on user input. At its core, PhysGen3D estimates 3D shapes, poses, physical and lighting properties of objects, thereby capturing essential physical attributes that drive realistic object interactions. This framework allows users to specify precise initial conditions, such as object speed or material properties, for enhanced control over generated video outcomes. We evaluate PhysGen3D's performance against closed-source state-of-the-art (SOTA) image-to-video models, including Pika, Kling, and Gen-3, showing PhysGen3D's capacity to generate videos with realistic physics while offering greater flexibility and fine-grained control. Our results show that PhysGen3D achieves a unique balance of photorealism, physical plausibility, and user-driven interactivity, opening new possibilities for generating dynamic, physics-grounded video from an image.

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Cited by 2 Pith papers

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

  1. PhysAgent: Reflective Agentic Physics Control for Physically Plausible Video Generation

    cs.CV 2026-07 conditional novelty 6.0

    Iteratively simulating, verifying, and repairing physics programs gives video generation more reliable fine-grained control over object motion than one-shot configuration.

  2. Physically Viable World Models: A Case for Query-Conditioned Embodied AI

    cs.AI 2026-05 unverdicted novelty 5.0

    Embodied AI requires query-conditioned world models that select the simplest physical abstraction sufficient to answer intervention queries.