Abstract: A method for constructing cartoonization models is provided. The method includes: generating a predetermined number of sample authentic images using a pre-trained first generative model; constructing a second generative model based on the first generative model, and generating a sample cartoon image corresponding to each of the sample authentic images using the second generative model; acquiring a sample image pair by combining the each of the sample authentic images with the corresponding sample cartoon image; and generating a cartoonization model for converting a target image into a fully cartoonized image by fitting, based on a sample set consisting of multiple sample image pairs, a predetermined initial cartoonization model with a weight corresponding to the second generative model as an initial weight.

Abstract: Provided is a method for training an image generation model, including: acquiring a first transformation model by training; acquiring a reconstruction model by training based on the first transformation model; acquiring a second transformation model by training; generating a grafted transformation model by grafting the first transformation model with the second transformation model; and generating the image generation model based on the reconstruction model and the grafted transformation model. The first transformation model is configured to generate a first training image according to a first noise sample. The first training image is an image of a first style. The reconstruction model is configured to map an original image sample to a latent variable corresponding to the original image sample. The second transformation model is configured to generate a second training image according to a second noise sample. The second training image is an image of a second style.

Abstract: A key frame scheduling method and apparatus include: performing feature extraction on a current frame through a first network layer of a neural network to obtain low-layer features of the current frame acquiring a scheduling probability of the current frame according to low-level features of a previous key frame adjacent to the current frame and the low-level features of the current frame; determining whether the current frame is scheduled as a key frame according to the scheduling probability value of the current frame; and when determining that the current frame is scheduled as a key frame, performing feature extraction on low-level features of a current key frame via a second network layer of the neural network to obtain high-level features of the current key frame, where the network depth of the first network layer is less than the network depth of the second network layer.

Abstract: A key frame scheduling method and apparatus include: performing feature extraction on a current frame through a first network layer of a neural network to obtain low-layer features of the current frame acquiring a scheduling probability of the current frame according to low-level features of a previous key frame adjacent to the current frame and the low-level features of the current frame; determining whether the current frame is scheduled as a key frame according to the scheduling probability value of the current frame; and when determining that the current frame is scheduled as a key frame, performing feature extraction on low-level features of a current key frame via a second network layer of the neural network to obtain high-level features of the current key frame, where the network depth of the first network layer is less than the network depth of the second network layer.