Amazon Technologies, Inc.

Aleix Margarit Martinez

Raghu Deep Gadde

Pietro Perona

Gerard Gjonej

Qianli Feng

Rui Chen

Alexandru I Indrei

Maksim Lapin

Anqi Liang

Guha Balakrishnan

Techniques are generally described for an artificial intelligence based interior designer system. In various examples, an image comprising a plurality of items arranged together in a room may be received. A visual representation and a positional representation of each item represented in the image may be determined. An output embedding may be generated based at least in part on the visual representation and the positional representation of each item represented in the image. At least one output item may be determined based at least in part on the output embedding. An image of the at least one output item may be displayed in association with the image comprising the plurality of items arranged together in the room.

Artificial intelligence interior design system

Described herein is a computer-implemented method for generating a synthetic image. An input image can be received by a computing device. A representation of the input image on an image approximation manifold can be identified by inputting the input image into a machine learning model. The image approximation manifold can be defined by the machine learning model. A local region of the image approximation manifold can be modified relative to the first representation to generate a modified image approximation manifold. The modified image approximation manifold can include a second representation of the input image. A synthetic image can be generated based on the modified image approximation manifold. A rendering of the synthetic image can be caused on a display.

Synthetic image generation by manifold modification of a machine learning model

Techniques are generally described for generation of synthetic image data. In some examples, a selection of a first image may be received. The first image may depict at least a first object having a plurality of image attributes representing visual characteristics of the at least the first object. In some examples, a selection of a first image attribute of the plurality of image attributes to be maintained in subsequently-generated images may be received. In various examples, a first machine learning model may generate a second image having the plurality of image attributes. The change in an appearance of the first image attribute may be minimized in the second image while a change in the appearance of other attributes of the plurality of image attributes may be maximized in the second image.

Generation of synthetic image data with varied attributes

Techniques are generally described for generation of photorealistic synthetic image data. A generator network generates first synthetic image data. A first class of image data represented by a first portion of the first synthetic image data is detected and the first portion is sent to a first discriminator network. The first discriminator network generates a prediction of whether the first portion of the first synthetic image data is synthetically generated. A second class of image data represented by a second portion of the first synthetic image data is detected and the second portion is sent to a second discriminator network. The second discriminator network generates a prediction of whether the second portion of the first synthetic image data is synthetically generated. The generator network is updated based on the predictions of the discriminators.

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Seattle, WA, United States of America

Aleix Margarit Martinez