Video Editing

V2VBench is a comprehensive benchmark designed to evaluate video editing methods. It consists of: - 50 standardized videos across 5 categories, and - 3 editing prompts per video, encompassing 4 editing tasks: Huggingface Datasets - 8 evaluation metrics to assess the quality of edited videos: Evaluation Metrics For detailed information, please refer to the accompanying paper.

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The benchmark for VPData, the largest video inpainting dataset, which comprises over 390K clips (> 866.7 hours) and features precise masks and detailed video captions.

The largest video inpainting dataset comprises over 390K clips (> 866.7 hours), featuring precise masks and detailed video captions.

The Temporal Logic Video (TLV) Dataset addresses the scarcity of state-of-the-art video datasets for long-horizon, temporally extended activity and object detection. It comprises two main components: Synthetic datasets: Generated by concatenating static images from established computer vision datasets (COCO and ImageNet), allowing for the introduction of a wide range of Temporal Logic (TL) specifications. Real-world datasets: Based on open-source autonomous vehicle (AV) driving datasets, specifically NuScenes and Waymo.