Summary of Aigcbench: Comprehensive Evaluation Of Image-to-video Content Generated by Ai, By Fanda Fan et al.
AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI
by Fanda Fan, Chunjie Luo, Wanling Gao, Jianfeng Zhan
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: This paper introduces AIGCBench, a comprehensive benchmark for evaluating video generation tasks, with a focus on Image-to-Video (I2V) generation. The proposed benchmark addresses limitations in existing benchmarks by featuring a diverse and open-domain image-text dataset that tests state-of-the-art algorithms under equivalent conditions. The authors employ a novel text combiner and GPT-4 to create rich text prompts for generating images via advanced Text-to-Image models. AIGCBench includes 11 metrics across four dimensions (control-video alignment, motion effects, temporal consistency, and video quality) to assess algorithm performance. These reference and video-free metrics provide a comprehensive evaluation strategy that correlates well with human judgment, offering insights into the strengths and weaknesses of current I2V algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper creates a new way to test how well computers can make videos from images. They made a big collection of images and text pairs to help compare different computer programs that make videos. The authors used special tools to create good prompts for the computers, which then generated images. To see how well the programs do, they came up with 11 ways to measure performance. These measures look at things like whether the video looks like it was made from an image and whether the motion in the video is smooth. This will help researchers make better videos. |
Keywords
» Artificial intelligence » Alignment » Gpt
