AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated
by AI
- URL: http://arxiv.org/abs/2401.01651v3
- Date: Tue, 23 Jan 2024 15:31:17 GMT
- Title: AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated
by AI
- Authors: Fanda Fan, Chunjie Luo, Wanling Gao, Jianfeng Zhan
- Abstract summary: This paper introduces AIGCBench, a pioneering comprehensive benchmark designed to evaluate a variety of video generation tasks.
A varied and open-domain image-text dataset that evaluates different state-of-the-art algorithms under equivalent conditions.
We employ a novel text combiner and GPT-4 to create rich text prompts, which are then used to generate images via advanced Text-to-Image models.
- Score: 1.1035305628305816
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The burgeoning field of Artificial Intelligence Generated Content (AIGC) is
witnessing rapid advancements, particularly in video generation. This paper
introduces AIGCBench, a pioneering comprehensive and scalable benchmark
designed to evaluate a variety of video generation tasks, with a primary focus
on Image-to-Video (I2V) generation. AIGCBench tackles the limitations of
existing benchmarks, which suffer from a lack of diverse datasets, by including
a varied and open-domain image-text dataset that evaluates different
state-of-the-art algorithms under equivalent conditions. We employ a novel text
combiner and GPT-4 to create rich text prompts, which are then used to generate
images via advanced Text-to-Image models. To establish a unified evaluation
framework for video generation tasks, our benchmark includes 11 metrics
spanning four dimensions to assess algorithm performance. These dimensions are
control-video alignment, motion effects, temporal consistency, and video
quality. These metrics are both reference video-dependent and video-free,
ensuring a comprehensive evaluation strategy. The evaluation standard proposed
correlates well with human judgment, providing insights into the strengths and
weaknesses of current I2V algorithms. The findings from our extensive
experiments aim to stimulate further research and development in the I2V field.
AIGCBench represents a significant step toward creating standardized benchmarks
for the broader AIGC landscape, proposing an adaptable and equitable framework
for future assessments of video generation tasks. We have open-sourced the
dataset and evaluation code on the project website:
https://www.benchcouncil.org/AIGCBench.
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