Generation method and device for dynamic picture
A dynamic picture and dynamic picture technology, applied in the network field, can solve the problems of single motion mode and low accuracy of dynamic pictures
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Embodiment 1
[0028] refer to figure 1 , which shows a flow chart of the steps of a method for generating a dynamic picture, including:
[0029] Step 101, generating a target vector, and using a prediction network to predict the target vector to obtain a foreground dynamic picture, a foreground mask dynamic picture and a background static picture.
[0030] Wherein, the target vector is a reference object for generating a dynamic picture. In practical applications, the target vector may be a randomly generated noise vector, or may be a vector obtained from a static picture, or may be a sum vector of a vector obtained from a static picture and a noise vector.
[0031] It can be understood that when the target vector is a noise vector, the generated dynamic picture has no reference, and the accuracy of the dynamic picture is low; when the target vector is a vector obtained from a static picture or a sum vector with the noise vector, the generated dynamic picture and The original static pictu...
Embodiment 2
[0046] The embodiment of the present application describes an optional dynamic picture generation method from the system architecture level.
[0047] refer to figure 2 , which shows a flow chart of specific steps of another method for generating a dynamic picture.
[0048] Step 201 , pre-training each network involved in the dynamic image generation step based on a preset dynamic image sample set.
[0049] Among them, the dynamic picture sample set includes a large number of dynamic pictures, the more the dynamic picture sample set, the longer the training time, the more accurate the training result; the smaller the dynamic picture sample set, the shorter the training time, the less accurate the training result.
[0050] It can be understood that the dynamic picture sample set can be obtained from the Internet or other methods. Embodiments of the present disclosure do not limit the manner of obtaining the dynamic picture sample set.
[0051] Specifically, first, a simulate...
Embodiment 3
[0097] refer to image 3 , which shows a structural diagram of a device for generating a dynamic picture, specifically as follows.
[0098] The foreground and background prediction module 301 is used to generate a target vector, and use a prediction network to predict the target vector to obtain a foreground dynamic picture, a foreground mask dynamic picture and a background static picture.
[0099] The splitting module 302 is configured to split the foreground dynamic picture and the foreground mask dynamic picture into at least one frame of foreground static picture and the corresponding foreground mask static picture.
[0100] The first dynamic prediction module 303 is used to input the first frame of the foreground static picture, the first frame of the foreground mask static picture and the background static picture into the first cell body of the long-term short-term memory network, and perform prediction to generate the first dynamic picture. One frame.
[0101] The s...
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