ProPhy: Progressive Physical Alignment for Dynamic World Simulation
Recent advances in video generation have shown remarkable potential for constructing world simulators. However, current models still struggle to produce physically consistent results, particularly when handling large-scale or complex dynamics. This limitation arises primarily because existing approaches respond isotropically to physical prompts and neglect the fine-grained alignment between generated content and localized physical cues. To address these challenges, we propose ProPhy, a Progressive Physical Alignment Framework that enables explicit physics-aware conditioning and anisotropic generation. ProPhy employs a two-stage Mixture-of-Physics-Experts (MoPE) mechanism for discriminative physical prior extraction, where Semantic Experts infer semantic-level physical principles from textual descriptions, and Refinement Experts capture token-level physical dynamics. This mechanism allows the model to learn fine-grained, physics-aware video representations that better reflect underlying physical laws. Furthermore, we introduce a physical alignment strategy that transfers the physical reasoning capabilities of vision-language models (VLMs) into the Refinement Experts, facilitating a more accurate representation of dynamic physical phenomena. Extensive experiments on physics-aware video generation benchmarks demonstrate that ProPhy produces more realistic, dynamic, and physically coherent results than existing state-of-the-art methods.
693639283962c926cf68073e
Zijun Wang
693639283962c926cf68073f
Panwen Hu
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Jing Wang
693639283962c926cf680741
Terry Jingchen Zhang
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Yuhao Cheng
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Long Chen
693639283962c926cf680744
Yiqiang Yan
693639283962c926cf680745
Zutao Jiang
693639283962c926cf680746
Hanhui Li
693639283962c926cf680747
Xiaodan Liang
ProPhy: Progressive Physical Alignment for Dynamic World Simulation
Recent advances in video generation have shown remarkable potential for constructing world simulators. However, current models still struggle to produce physically consistent results, particularly when handling large-scale or complex dynamics. This limitation arises primarily because existing approaches respond isotropically to physical prompts and neglect the fine-grained alignment between generated content and localized physical cues. To address these challenges, we propose ProPhy, a Progressive Physical Alignment Framework that enables explicit physics-aware conditioning and anisotropic generation. ProPhy employs a two-stage Mixture-of-Physics-Experts (MoPE) mechanism for discriminative physical prior extraction, where Semantic Experts infer semantic-level physical principles from textual descriptions, and Refinement Experts capture token-level physical dynamics. This mechanism allows the model to learn fine-grained, physics-aware video representations that better reflect underlying physical laws. Furthermore, we introduce a physical alignment strategy that transfers the physical reasoning capabilities of vision-language models (VLMs) into the Refinement Experts, facilitating a more accurate representation of dynamic physical phenomena. Extensive experiments on physics-aware video generation benchmarks demonstrate that ProPhy produces more realistic, dynamic, and physically coherent results than existing state-of-the-art methods.
ProPhy, a two-stage framework with Semantic and Refinement Experts, enhances video generation by incorporating physics-aware conditioning and anisotropic generation, improving physical consistency and realism.
video generation world simulators physical consistency large-scale dynamics complex dynamics ProPhy Progressive Physical Alignment Framework Mixture-of-Physics-Experts (MoPE) Semantic Experts Refinement Experts semantic-level physical principles token-level physical dynamics physical reasoning capabilities vision-language models (VLMs) physics-aware video representations dynamic physical phenomena