Omnimatte: Associating Objects and Their Effects in Video

Victoria D. Doty

Present computer system vision technologies can segment objects in photos and video clips on the other hand, minor notice has been compensated to deciding the outcomes of objects, for instance, shadows of a human on the ground and distant partitions or reflections in windows.

A modern review proposes a technique to determine those people outcomes in video clips.

Video enhancing. Graphic credit: DaleshTV by using Wikimedia, CC-BY-SA-four.

Offered an enter movie with segmented going topics, the design provides an opacity map and shade image that consists of the subject and segment regions correlated with it. The method uses self-supervised teaching with no observing further illustrations. It can detect the outcomes of a selection of objects, like animals, cars and trucks, and folks, and captures unique outcomes this sort of as shadows, reflections, dust, and smoke. The introduced job can be beneficial in domains of movie enhancing as item elimination or history substitute.

Computer vision is progressively effective at segmenting objects in photos and video clips on the other hand, scene outcomes associated to the objects—shadows, reflections, created smoke, etc—are usually neglected. Identifying this sort of scene outcomes and associating them with the objects manufacturing them is vital for improving our essential knowledge of visual scenes, and can also support a selection of programs this sort of as eradicating, duplicating, or improving objects in movie. In this function, we take a step towards resolving this novel challenge of routinely associating objects with their outcomes in movie. Offered an common movie and a tough segmentation mask around time of one or extra topics of fascination, we estimate an omnimatte for each individual subject—an alpha matte and shade image that consists of the subject together with all its associated time-varying scene things. Our design is qualified only on the enter movie in a self-supervised manner, with no any manual labels, and is generic—it provides omnimattes routinely for arbitrary objects and a selection of outcomes. We demonstrate final results on genuine-planet video clips that contains interactions involving unique varieties of topics (cars and trucks, animals, folks) and sophisticated outcomes, ranging from semi-transparent things this sort of as smoke and reflections, to fully opaque outcomes this sort of as objects hooked up to the subject.

Investigate paper: Lu, E., Cole, F., Dekel, T., Zisserman, A., Freeman, W. T., and Rubinstein, M., “Omnimatte: Associating Objects and Their Outcomes in Video”, 2021. Link:

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