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BlobGAN: Spatially Disentangled Scene Representations

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

We propose an unsupervised, mid-level representation for a generative model of scenes. The representation is mid-level in that it is neither per-pixel nor per-image; rather, scenes are modeled as a collection of spatial, depth-ordered “blobs” of features. Blobs are differentiably placed onto a feature grid that is decoded into an image by a generative adversarial network. Due to the spatial uniformity of blobs and the locality inherent to convolution, our network learns to associate different blobs with different entities in a scene and to arrange these blobs to capture scene layout. We demonstrate this emergent behavior by showing that, despite training without any supervision, our method enables applications such as easy manipulation of objects within a scene (e.g. moving, removing, and restyling furniture), creation of feasible scenes given constraints (e.g. plausible rooms with drawers at a particular location), and parsing of real-world images into constituent parts. On a challenging multi-category dataset of indoor scenes, BlobGAN outperforms StyleGAN2 in image quality as measured by FID. See our project page for video results and interactive demo: http://www.dave.ml/blobgan.

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Acknowledgements

We thank Allan Jabri, Assaf Shocher, Bill Peebles, Tim Brooks, and Yossi Gandelsman for endless insightful discussions and important feedback, and especially thank Vickie Ye for advice on blob compositing, splatting, and visualization. Thanks also to Georgios Pavlakos for deadline-week pixel inpsection and Shiry Ginosar for post-deadline-week guidance and helpful comments. Research was supported in part by the DARPA MCS program and a gift from Adobe Research. This work was started while DE was an intern at Adobe.

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Epstein, D., Park, T., Zhang, R., Shechtman, E., Efros, A.A. (2022). BlobGAN: Spatially Disentangled Scene Representations. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13675. Springer, Cham. https://doi.org/10.1007/978-3-031-19784-0_36

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