Object-centric image generation with factored depths, locations, and appearances

T. Anciukevicius, C. Lampert, P.M. Henderson, ArXiv (n.d.).

Preprint | Submitted | English
Department
Abstract
We present a generative model of images that explicitly reasons over the set of objects they show. Our model learns a structured latent representation that separates objects from each other and from the background; unlike prior works, it explicitly represents the 2D position and depth of each object, as well as an embedding of its segmentation mask and appearance. The model can be trained from images alone in a purely unsupervised fashion without the need for object masks or depth information. Moreover, it always generates complete objects, even though a significant fraction of training images contain occlusions. Finally, we show that our model can infer decompositions of novel images into their constituent objects, including accurate prediction of depth ordering and segmentation of occluded parts.
Publishing Year
Date Published
2020-04-01
Journal Title
ArXiv
Article Number
2004.00642
Page
24
IST-REx-ID

Cite this

Anciukevicius T, Lampert C, Henderson PM. Object-centric image generation with factored depths, locations, and appearances. ArXiv.
Anciukevicius, T., Lampert, C., & Henderson, P. M. (n.d.). Object-centric image generation with factored depths, locations, and appearances. ArXiv. ArXiv.
Anciukevicius, Titas, Christoph Lampert, and Paul M Henderson. “Object-Centric Image Generation with Factored Depths, Locations, and Appearances.” ArXiv. ArXiv, n.d.
T. Anciukevicius, C. Lampert, and P. M. Henderson, “Object-centric image generation with factored depths, locations, and appearances,” ArXiv. ArXiv.
Anciukevicius T, Lampert C, Henderson PM. Object-centric image generation with factored depths, locations, and appearances. ArXiv.
Anciukevicius, Titas, et al. “Object-Centric Image Generation with Factored Depths, Locations, and Appearances.” ArXiv, 2004.00642, ArXiv.
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