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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.04221 |
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| _version_ | 1866915665770708992 |
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| author | Bai, Xiangyu Liang, He Galoaa, Bishoy Nandi, Utsav Moezzi, Shayda He, Yuhang Ostadabbas, Sarah |
| author_facet | Bai, Xiangyu Liang, He Galoaa, Bishoy Nandi, Utsav Moezzi, Shayda He, Yuhang Ostadabbas, Sarah |
| contents | While text-to-video (T2V) generation has achieved remarkable progress in photorealism, generating intent-aligned videos that faithfully obey physics principles remains a core challenge. In this work, we systematically study Newtonian motion-controlled text-to-video generation and evaluation, emphasizing physical precision and motion coherence. We introduce MoReGen, a motion-aware, physics-grounded T2V framework that integrates multi-agent LLMs, physics simulators, and renderers to generate reproducible, physically accurate videos from text prompts in the code domain. To quantitatively assess physical validity, we propose object-trajectory correspondence as a direct evaluation metric and present MoReSet, a benchmark of 1,275 human-annotated videos spanning nine classes of Newtonian phenomena with scene descriptions, spatiotemporal relations, and ground-truth trajectories. Using MoReSet, we conduct experiments on existing T2V models, evaluating their physical validity through both our MoRe metrics and existing physics-based evaluators. Our results reveal that state-of-the-art models struggle to maintain physical validity, while MoReGen establishes a principled direction toward physically coherent video synthesis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04221 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MoReGen: Multi-Agent Motion-Reasoning Engine for Code-based Text-to-Video Synthesis Bai, Xiangyu Liang, He Galoaa, Bishoy Nandi, Utsav Moezzi, Shayda He, Yuhang Ostadabbas, Sarah Computer Vision and Pattern Recognition While text-to-video (T2V) generation has achieved remarkable progress in photorealism, generating intent-aligned videos that faithfully obey physics principles remains a core challenge. In this work, we systematically study Newtonian motion-controlled text-to-video generation and evaluation, emphasizing physical precision and motion coherence. We introduce MoReGen, a motion-aware, physics-grounded T2V framework that integrates multi-agent LLMs, physics simulators, and renderers to generate reproducible, physically accurate videos from text prompts in the code domain. To quantitatively assess physical validity, we propose object-trajectory correspondence as a direct evaluation metric and present MoReSet, a benchmark of 1,275 human-annotated videos spanning nine classes of Newtonian phenomena with scene descriptions, spatiotemporal relations, and ground-truth trajectories. Using MoReSet, we conduct experiments on existing T2V models, evaluating their physical validity through both our MoRe metrics and existing physics-based evaluators. Our results reveal that state-of-the-art models struggle to maintain physical validity, while MoReGen establishes a principled direction toward physically coherent video synthesis. |
| title | MoReGen: Multi-Agent Motion-Reasoning Engine for Code-based Text-to-Video Synthesis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.04221 |