PhyMAGIC: Physical Motion-Aware Generative Inference with Confidence-guided LLM

• URL: http://arxiv.org/abs/2505.16456v2
• Date: Thu, 25 Sep 2025 22:17:16 GMT
• Title: PhyMAGIC: Physical Motion-Aware Generative Inference with Confidence-guided LLM
• Authors: Siwei Meng, Yawei Luo, Ping Liu,
• Abstract summary: We present PhyMAGIC, a training-free framework that generates physically consistent motion from a single image.<n>PhyMAGIC integrates a pre-trained image-to-video diffusion model, confidence-guided reasoning via LLMs, and a differentiable physics simulator.<n> Comprehensive experiments demonstrate that PhyMAGIC outperforms state-of-the-art video generators and physics-aware baselines.
• Score: 17.554471769834453
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in 3D content generation have amplified demand for dynamic models that are both visually realistic and physically consistent. However, state-of-the-art video diffusion models frequently produce implausible results such as momentum violations and object interpenetrations. Existing physics-aware approaches often rely on task-specific fine-tuning or supervised data, which limits their scalability and applicability. To address the challenge, we present PhyMAGIC, a training-free framework that generates physically consistent motion from a single image. PhyMAGIC integrates a pre-trained image-to-video diffusion model, confidence-guided reasoning via LLMs, and a differentiable physics simulator to produce 3D assets ready for downstream physical simulation without fine-tuning or manual supervision. By iteratively refining motion prompts using LLM-derived confidence scores and leveraging simulation feedback, PhyMAGIC steers generation toward physically consistent dynamics. Comprehensive experiments demonstrate that PhyMAGIC outperforms state-of-the-art video generators and physics-aware baselines, enhancing physical property inference and motion-text alignment while maintaining visual fidelity.

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