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Animation drawing auxiliary creation method based on GAN

A painting and line draft technology, applied in the field of GAN-based animation painting auxiliary creation, can solve the problems of low automatic coloring intelligence, unreasonable color, insufficient interactive coloring effect and user experience, so as to improve the creative experience and improve the user experience. The effect of artistic aesthetics

Active Publication Date: 2019-10-25
COMMUNICATION UNIVERSITY OF CHINA
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The coloring of the generated results of the first generation is relatively natural, but because vgg covers the topological space information corresponding to the color of the reference picture, the color structure of the picture to be colored is over-fitted, and it cannot be well-adapted to coloring
The third generation of PaintsTransfer is able to produce visually good shading results, but the local details of the color are dirty
[0013] Traditional animation creation requires professional painting skills and rich coloring experience, and creating high-quality animation images is time-consuming and labor-intensive
At present, there are some problems that need to be optimized in the existing animation creation system, such as the professional enhancement of the line draft, the unreasonable spread of the color of the line draft coloring, the low level of intelligence of the automatic coloring, the effect of interactive coloring and the lack of user experience. , Full-process and efficient generation of animation music albums, etc.

Method used

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  • Animation drawing auxiliary creation method based on GAN
  • Animation drawing auxiliary creation method based on GAN
  • Animation drawing auxiliary creation method based on GAN

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Embodiment Construction

[0047] In order for those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention are described clearly and completely below.

[0048] In some processes described in the specification and claims of the present invention, a plurality of operations appearing in a specific order are included, but it should be clearly understood that these operations may not be performed in the order in which they appear herein or performed in parallel, the operation The serial numbers of steps such as step a, step b, etc. are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. Additionally, these flows may include more or fewer operations, and these operations may be performed sequentially or in parallel.

[0049] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, t...

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Abstract

The invention discloses an animation drawing auxiliary creation method based on a GAN. An integrated cartoon drawing auxiliary creation system is constructed by utilizing line draft professional enhancement technology based on generative adversarial network and reinforcement learning, line draft coloring technology based on related genus learning and color clustering, cartoon music album generation technology based on audio-visual association and emotion calculation and the like, so that the creation experience and artistic aesthetic appreciation of users are improved.

Description

technical field [0001] The present invention relates to the technical field of technical image generation, and more specifically, relates to a GAN-based animation drawing assistant creation method. Background technique [0002] At present, there are very rich image translation and style transfer tasks. Regardless of realistic or non-realistic rendering, it is to restore information or modify features of missing or to-be-transformed images. The deep learning network used in image generation is generally divided into variational autoencoder (VAE), generative confrontation network (GAN) and their variants. Because VAE has no confrontational learning, the generated pictures are blurred. The image generation of GAN and its variants is more widely used and the effect is better. [0003] Conditional GAN ​​adds some explicit external information to the original GAN, which is generally additional input from the user, such as semantic categories, high-dimensional categories, and und...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T13/00G06N3/04G06N3/08
CPCG06T13/00G06N3/08G06N3/045Y02P90/30
Inventor 任慧李佳苏志斌李真蒋玉暕
Owner COMMUNICATION UNIVERSITY OF CHINA
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