GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914031515729920 |
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| author | Kang, Jenna Silva, Maria Sangkloy, Patsorn Chen, Kenneth Williams, Niall Sun, Qi |
| author_facet | Kang, Jenna Silva, Maria Sangkloy, Patsorn Chen, Kenneth Williams, Niall Sun, Qi |
| contents | Recent advances in probabilistic generative models have extended capabilities from static image synthesis to text-driven video generation. However, the inherent randomness of their generation process can lead to unpredictable artifacts, such as impossible physics and temporal inconsistency. Progress in addressing these challenges requires systematic benchmarks, yet existing datasets primarily focus on generative images due to the unique spatio-temporal complexities of videos. To bridge this gap, we introduce GeneVA, a large-scale artifact dataset with rich human annotations that focuses on spatio-temporal artifacts in videos generated from natural text prompts. We hope GeneVA can enable and assist critical applications, such as benchmarking model performance and improving generative video quality. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_08818 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Kang, Jenna Silva, Maria Sangkloy, Patsorn Chen, Kenneth Williams, Niall Sun, Qi Computer Vision and Pattern Recognition Recent advances in probabilistic generative models have extended capabilities from static image synthesis to text-driven video generation. However, the inherent randomness of their generation process can lead to unpredictable artifacts, such as impossible physics and temporal inconsistency. Progress in addressing these challenges requires systematic benchmarks, yet existing datasets primarily focus on generative images due to the unique spatio-temporal complexities of videos. To bridge this gap, we introduce GeneVA, a large-scale artifact dataset with rich human annotations that focuses on spatio-temporal artifacts in videos generated from natural text prompts. We hope GeneVA can enable and assist critical applications, such as benchmarking model performance and improving generative video quality. |
| title | GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.08818 |