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arXiv · 2611.00430

Real-VAS: a Real-World Video Amodal Segmentation Dataset

Finlay G. C. Hudson·William A. P. Smith
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Abstract

Amodal video object segmentation is fundamentally limited by the absence of datasets that combine real-world complexity with precise ground-truth annotations. To address this, we present Real Video Amodal Segmentation (Real-VAS), a new large-scale, zero-shot evaluation dataset built from composited real-image video. We introduce a novel data generation pipeline that enables the creation of programmatically consistent amodal ground truth for real-world footage, in which the object mask, occlusion boundary, and composited image are all derived from a single unoccluded source, without relying on human estimation or expensive 3D reconstruction. Our dataset is structured into two challenging scenarios: dynamic Occlusion, created by compositing two clips of moving objects, and a unique Container category featuring complex, physically constrained interactions. These container scenarios--On Surface Containment, Articulated Containment, and Mobile Containment--allow us to generate precise ground truth by simulating an object's motion based on its container's tracked transformation. As a result, Real-VAS provides a diverse and challenging benchmark for evaluating amodal segmentation models on realistic video with the precision of synthetic data. The dataset and our generation code will be made publicly available.

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Finlay G. C. Hudson, William A. P. Smith. 2026-10-30. Real-VAS: a Real-World Video Amodal Segmentation Dataset. https://arxiv.org/abs/2611.00430

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