A video style transformation and automatic generation method and system based on depth learning

Abstract

The invention provides a video style transformation and automatic generation method based on depth learning. The method comprises the steps of utilizing a depth learning framework to classify a shooting subject and to train a style model; acquiring video to be processed and user video requirements; identifying the category of the video to be processed, and recommending the optimal number of imagesand video materials according to the user style and duration requirements; according to the video material and the video material order selected by the user, automatically rendering the video fragments and generating the style video according to the corresponding style processing parameters by a render. After that, the user adjusts the modification information as the feedback information to optimize the style model, analyzes the user preferences, optimizes the depth learning framework, and forms the feedback learning model. The invention also provides a video style transformation and automatic generation system based on depth learning, which automatically recommends style materials according to the video style requirements of a user, automatically completes the style rendering of a videosegment and the rendering of an overall video, and generates a video of a specific style.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a method and system for video style transformation and automatic generation based on deep learning. Background technique [0002] In the fields related to video production, there is currently no innovative application based on artificial intelligence, and there is no set of feasible solutions to meet the needs of video design. In addition, most of the video processing is in the form of templates with preset parameters, which has obvious defects: 1. The number of templates is limited, and the realization effect of templates is limited to the template parameters that have been set. 2. The setting of the template is based on the long-term accumulation of professional knowledge, which increases the learning cost of video production. 3. There is a lack of consideration of uniformity in the editing of video materials with different tones and tones. 4. Video productio...

AI Technical Summary

Problems solved by technology

[0002] In the fields related to video production, innovative applications based on artificial intelligence are still in a blank state, and there is no set of feasible solutions to meet the needs of video design

In addition, most of the video processing is in the form of templates with preset parameters, which has obvious defects: 1. The number of templates is limited, and the realization effect of templates is limited to the template parameters that have been set

2. The setting of the template is based on the long-term accumulation of professional knowledge, which increases the learning cost of video production

3. There is a lack of uniformity in the editing of video materials with different tones and tones

4. Video production in the form of templates cannot better meet the needs of non-professional groups for professional video production

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