VideoPhy: Evaluating Physical Commonsense for Video Generation
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913528512774144 |
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| author | Bansal, Hritik Lin, Zongyu Xie, Tianyi Zong, Zeshun Yarom, Michal Bitton, Yonatan Jiang, Chenfanfu Sun, Yizhou Chang, Kai-Wei Grover, Aditya |
| author_facet | Bansal, Hritik Lin, Zongyu Xie, Tianyi Zong, Zeshun Yarom, Michal Bitton, Yonatan Jiang, Chenfanfu Sun, Yizhou Chang, Kai-Wei Grover, Aditya |
| contents | Recent advances in internet-scale video data pretraining have led to the development of text-to-video generative models that can create high-quality videos across a broad range of visual concepts, synthesize realistic motions and render complex objects. Hence, these generative models have the potential to become general-purpose simulators of the physical world. However, it is unclear how far we are from this goal with the existing text-to-video generative models. To this end, we present VideoPhy, a benchmark designed to assess whether the generated videos follow physical commonsense for real-world activities (e.g. marbles will roll down when placed on a slanted surface). Specifically, we curate diverse prompts that involve interactions between various material types in the physical world (e.g., solid-solid, solid-fluid, fluid-fluid). We then generate videos conditioned on these captions from diverse state-of-the-art text-to-video generative models, including open models (e.g., CogVideoX) and closed models (e.g., Lumiere, Dream Machine). Our human evaluation reveals that the existing models severely lack the ability to generate videos adhering to the given text prompts, while also lack physical commonsense. Specifically, the best performing model, CogVideoX-5B, generates videos that adhere to the caption and physical laws for 39.6% of the instances. VideoPhy thus highlights that the video generative models are far from accurately simulating the physical world. Finally, we propose an auto-evaluator, VideoCon-Physics, to assess the performance reliably for the newly released models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_03520 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VideoPhy: Evaluating Physical Commonsense for Video Generation Bansal, Hritik Lin, Zongyu Xie, Tianyi Zong, Zeshun Yarom, Michal Bitton, Yonatan Jiang, Chenfanfu Sun, Yizhou Chang, Kai-Wei Grover, Aditya Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Recent advances in internet-scale video data pretraining have led to the development of text-to-video generative models that can create high-quality videos across a broad range of visual concepts, synthesize realistic motions and render complex objects. Hence, these generative models have the potential to become general-purpose simulators of the physical world. However, it is unclear how far we are from this goal with the existing text-to-video generative models. To this end, we present VideoPhy, a benchmark designed to assess whether the generated videos follow physical commonsense for real-world activities (e.g. marbles will roll down when placed on a slanted surface). Specifically, we curate diverse prompts that involve interactions between various material types in the physical world (e.g., solid-solid, solid-fluid, fluid-fluid). We then generate videos conditioned on these captions from diverse state-of-the-art text-to-video generative models, including open models (e.g., CogVideoX) and closed models (e.g., Lumiere, Dream Machine). Our human evaluation reveals that the existing models severely lack the ability to generate videos adhering to the given text prompts, while also lack physical commonsense. Specifically, the best performing model, CogVideoX-5B, generates videos that adhere to the caption and physical laws for 39.6% of the instances. VideoPhy thus highlights that the video generative models are far from accurately simulating the physical world. Finally, we propose an auto-evaluator, VideoCon-Physics, to assess the performance reliably for the newly released models. |
| title | VideoPhy: Evaluating Physical Commonsense for Video Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.03520 |