Image generation method and apparatus and neural network training method and apparatus
AssigneeRicoh Company, Ltd.
InventorYing ZHAO
Life of this patent
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Abstract
A method of generating an image is provided. The method includes a step of obtaining a first target image for image generation; a step of obtaining a reference depth image indicating a reference depth feature of the first target image as well as a reference edge image indicating a reference edge feature of the first target image; and a step of generating, based on the reference depth image and the reference edge image, a first depth image and a first edge image corresponding to the first target image by utilizing a first generative neural network.
Claims20
A method of generating an image, comprising:
- obtaining a first target image for image generation;
- obtaining a reference depth image indicating a reference depth feature of the first target image as well as a reference edge image indicating a reference edge feature of the first target image;
- and
- generating, based on the reference depth image and the reference edge image, a first depth image and a first edge image corresponding to the first target image by utilizing a first generative neural network.
An apparatus for generating an image, comprising:
- a processor;
- and
- a memory coupled to the processor, storing a computer program,
- wherein, the computer program causes, when executed by the processor, the processor to implement
- obtaining a first target image for image generation;
- obtaining a reference depth image indicating a reference depth feature of the first target image as well as a reference edge image indicating a reference edge feature of the first target image;
- and
- generating, based on the reference depth image and the reference edge image, a first depth image and a first edge image corresponding to the first target image by utilizing a first generative neural network.
A method of training a neural network, comprising:
- inputting a first training target image as well as a first training depth image indicating a reference depth feature of the first training target image and a first training edge image indicating a reference edge feature of the first training target image;
- and
- generating, based on the first training depth image and the first training edge image, a first training depth generation image and a first training edge generation image corresponding to the first training target image by utilizing a first generative neural network, so as to train the first generative neural network and adjust a parameter(s) of the first generative neural network.
Dependencies are inferred from the claim text.