TY - JOUR
T1 - Interactive scene generation via scene graphs with attributes
AU - Ashual, Oron
AU - Wolf, Lior
N1 - Publisher Copyright:
© 2020, Association for the Advancement of Artificial Intelligence.
PY - 2020
Y1 - 2020
N2 - We introduce a simple yet expressive image generation method. On the one hand, it does not require the user to paint the masks or define a bounding box of the various objects, since the model does it by itself. On the other hand, it supports defining a coarse location and size of each object. Based on this, we offer a simple, interactive GUI, that allows a layman user to generate diverse images effortlessly. From a technical perspective, we introduce a dual embedding of layout and appearance. In this scheme, the location, size, and appearance of an object can change independently of each other. This way, the model is able to generate innumerable images per scene graph, to better express the intention of the user. In comparison to previous work, we also offer better quality and higher resolution outputs. This is due to a superior architecture, which is based on a novel set of discriminators. Those discriminators better constrain the shape of the generated mask, as well as capturing the appearance encoding in a counterfactual way. Our code is publicly available at https://www.github.com/ ashual/scene generation.
AB - We introduce a simple yet expressive image generation method. On the one hand, it does not require the user to paint the masks or define a bounding box of the various objects, since the model does it by itself. On the other hand, it supports defining a coarse location and size of each object. Based on this, we offer a simple, interactive GUI, that allows a layman user to generate diverse images effortlessly. From a technical perspective, we introduce a dual embedding of layout and appearance. In this scheme, the location, size, and appearance of an object can change independently of each other. This way, the model is able to generate innumerable images per scene graph, to better express the intention of the user. In comparison to previous work, we also offer better quality and higher resolution outputs. This is due to a superior architecture, which is based on a novel set of discriminators. Those discriminators better constrain the shape of the generated mask, as well as capturing the appearance encoding in a counterfactual way. Our code is publicly available at https://www.github.com/ ashual/scene generation.
UR - http://www.scopus.com/inward/record.url?scp=85106533472&partnerID=8YFLogxK
U2 - 10.1609/aaai.v34i09.7112
DO - 10.1609/aaai.v34i09.7112
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AN - SCOPUS:85106533472
SN - 2159-5399
VL - 34
SP - 13651
EP - 13654
JO - Proceedings of the AAAI Conference on Artificial Intelligence
JF - Proceedings of the AAAI Conference on Artificial Intelligence
IS - 09
T2 - 34th AAAI Conference on Artificial Intelligence, AAAI 2020
Y2 - 7 February 2020 through 12 February 2020
ER -