Summary of Videophy: Evaluating Physical Commonsense For Video Generation, by Hritik Bansal et al.
VideoPhy: Evaluating Physical Commonsense for Video Generation
by Hritik Bansal, Zongyu Lin, Tianyi Xie, Zeshun Zong, Michal Yarom, Yonatan Bitton, Chenfanfu Jiang, Yizhou Sun, Kai-Wei Chang, Aditya Grover
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary 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. The potential for these models to become general-purpose simulators of the physical world is significant. However, it is unclear how far we are from this goal with the existing text-to-video generative models. To address this uncertainty, we present VideoPhy, a benchmark designed to assess whether generated videos follow physical commonsense for real-world activities. We curate diverse prompts that involve interactions between various material types in the physical world and generate videos conditioned on these captions using state-of-the-art text-to-video generative models, including open models like CogVideoX and closed models like Lumiere and Dream Machine. Our human evaluation reveals that existing models severely lack the ability to generate videos adhering to given text prompts and lack physical commonsense. Specifically, the best performing model, CogVideoX-5B, generates videos that adhere to captions and physical laws for 39.6% of instances. VideoPhy highlights that video generative models are far from accurately simulating the physical world. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create realistic videos of everyday activities just by typing a description! This technology has made huge progress in recent years, but it’s not perfect yet. To test how well these models do, we created a special set of prompts that involve different materials and activities, like marbles rolling down a slope or water flowing through pipes. We then asked the models to generate videos based on these prompts using state-of-the-art technology. Our results show that even the best models struggle to create videos that accurately depict real-world events and follow basic physical laws. This is a big problem because we want these models to be able to simulate real-life situations, like accidents or natural disasters, in order to prepare for them. |
Keywords
» Artificial intelligence » Pretraining
