VDOR: A Video-based Dataset for Object Removal via Sequence Consistency

• URL: http://arxiv.org/abs/2501.07397v2
• Date: Fri, 31 Jan 2025 06:41:24 GMT
• Title: VDOR: A Video-based Dataset for Object Removal via Sequence Consistency
• Authors: Runpu Wei, Shuo Zhang, Zhonghao Yan, Zijin Yin, Xueyi Wang, Kongming Liang, Zhanyu Ma,
• Abstract summary: Existing datasets related to object removal serve a valuable foundation for model validation and optimization.<n>We propose a novel video-based annotation pipeline for constructing a realistic illumination-aware object removal dataset.<n>By leveraging continuous real-world video frames, we minimize distribution gaps and accurately capture realistic lighting and shadow variations.
• Score: 19.05827956984347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object removal, as a sub-task of image inpainting, has garnered significant attention in recent years. Existing datasets related to object removal serve a valuable foundation for model validation and optimization. However, they mainly rely on inpainting techniques to generate pseudo-removed results, leading to distribution gaps between synthetic and real-world data. While some real-world datasets mitigate these issues, they face challenges such as limited scalability, high annotation costs, and unrealistic representations of lighting and shadows. To address these limitations, we propose a novel video-based annotation pipeline for constructing a realistic illumination-aware object removal dataset. Leveraging this pipeline, we introduce VDOR, a dataset specifically designed for object removal tasks, which comprises triplets of original frame images with objects, background images without objects, and corresponding masks. By leveraging continuous real-world video frames, we minimize distribution gaps and accurately capture realistic lighting and shadow variations, ensuring close alignment with real-world scenarios. Our approach significantly reduces annotation effort while providing a robust foundation for advancing object removal research.

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