VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910706098503680 |
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| author | Huang, Ziqi Zhang, Fan Xu, Xiaojie He, Yinan Yu, Jiashuo Dong, Ziyue Ma, Qianli Chanpaisit, Nattapol Si, Chenyang Jiang, Yuming Wang, Yaohui Chen, Xinyuan Chen, Ying-Cong Wang, Limin Lin, Dahua Qiao, Yu Liu, Ziwei |
| author_facet | Huang, Ziqi Zhang, Fan Xu, Xiaojie He, Yinan Yu, Jiashuo Dong, Ziyue Ma, Qianli Chanpaisit, Nattapol Si, Chenyang Jiang, Yuming Wang, Yaohui Chen, Xinyuan Chen, Ying-Cong Wang, Limin Lin, Dahua Qiao, Yu Liu, Ziwei |
| contents | Video generation has witnessed significant advancements, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable for two reasons: 1) Existing metrics do not fully align with human perceptions; 2) An ideal evaluation system should provide insights to inform future developments of video generation. To this end, we present VBench, a comprehensive benchmark suite that dissects "video generation quality" into specific, hierarchical, and disentangled dimensions, each with tailored prompts and evaluation methods. VBench has several appealing properties: 1) Comprehensive Dimensions: VBench comprises 16 dimensions in video generation (e.g., subject identity inconsistency, motion smoothness, temporal flickering, and spatial relationship, etc). The evaluation metrics with fine-grained levels reveal individual models' strengths and weaknesses. 2) Human Alignment: We also provide a dataset of human preference annotations to validate our benchmarks' alignment with human perception, for each evaluation dimension respectively. 3) Valuable Insights: We look into current models' ability across various evaluation dimensions, and various content types. We also investigate the gaps between video and image generation models. 4) Versatile Benchmarking: VBench++ supports evaluating text-to-video and image-to-video. We introduce a high-quality Image Suite with an adaptive aspect ratio to enable fair evaluations across different image-to-video generation settings. Beyond assessing technical quality, VBench++ evaluates the trustworthiness of video generative models, providing a more holistic view of model performance. 5) Full Open-Sourcing: We fully open-source VBench++ and continually add new video generation models to our leaderboard to drive forward the field of video generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_13503 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models Huang, Ziqi Zhang, Fan Xu, Xiaojie He, Yinan Yu, Jiashuo Dong, Ziyue Ma, Qianli Chanpaisit, Nattapol Si, Chenyang Jiang, Yuming Wang, Yaohui Chen, Xinyuan Chen, Ying-Cong Wang, Limin Lin, Dahua Qiao, Yu Liu, Ziwei Computer Vision and Pattern Recognition Video generation has witnessed significant advancements, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable for two reasons: 1) Existing metrics do not fully align with human perceptions; 2) An ideal evaluation system should provide insights to inform future developments of video generation. To this end, we present VBench, a comprehensive benchmark suite that dissects "video generation quality" into specific, hierarchical, and disentangled dimensions, each with tailored prompts and evaluation methods. VBench has several appealing properties: 1) Comprehensive Dimensions: VBench comprises 16 dimensions in video generation (e.g., subject identity inconsistency, motion smoothness, temporal flickering, and spatial relationship, etc). The evaluation metrics with fine-grained levels reveal individual models' strengths and weaknesses. 2) Human Alignment: We also provide a dataset of human preference annotations to validate our benchmarks' alignment with human perception, for each evaluation dimension respectively. 3) Valuable Insights: We look into current models' ability across various evaluation dimensions, and various content types. We also investigate the gaps between video and image generation models. 4) Versatile Benchmarking: VBench++ supports evaluating text-to-video and image-to-video. We introduce a high-quality Image Suite with an adaptive aspect ratio to enable fair evaluations across different image-to-video generation settings. Beyond assessing technical quality, VBench++ evaluates the trustworthiness of video generative models, providing a more holistic view of model performance. 5) Full Open-Sourcing: We fully open-source VBench++ and continually add new video generation models to our leaderboard to drive forward the field of video generation. |
| title | VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.13503 |