Abstract: Embodiments of this disclosure include an image enhancement method and apparatus. The image enhancement may include obtaining an original image and performing synthesis processing on features of the original image to obtain a first illumination map corresponding to the original image. A resolution of the first illumination map may be lower than a resolution of the original image. The image enhancement may further include obtaining, based on the first illumination map, a mapping relationship between an image to an illumination map and performing mapping processing on the original image based on the mapping relationship to obtain a second illumination map. A resolution of the second illumination map may be equal to the resolution of the original image. The image enhancement may further include performing image enhancement processing on the original image according to the second illumination map to obtain a target image.

Abstract: A method for training an SMPL parameter prediction model, including: obtaining a sample picture; inputting the sample picture into a pose parameter prediction model to obtain a predicted pose parameter; inputting the sample picture into a shape parameter prediction model to obtain a predicted shape parameter; calculating model prediction losses according to an SMPL parameter prediction model and annotation information of the sample picture; and updating the pose parameter prediction model and the shape parameter prediction model according to the model prediction losses.

Abstract: Embodiments of this application disclose a method and an apparatus for reconstructing a three-dimensional model of a human body, and a storage medium. The method includes obtaining a two-dimensional image of a human body part to be reconstructed; mapping the two-dimensional image to a voxel space, to obtain a voxelized result of the two-dimensional image; obtaining three-dimensional morphable model parameters corresponding to the two-dimensional image based on calculating the voxelized result using a preset three-dimensional parameter regression network; and reconstructing a three-dimensional model of the human body part based on the three-dimensional morphable model parameters.

Abstract: Embodiments of the present disclosure disclose a face model processing method performed at an electronic device. The method includes the following steps: obtaining a three-dimensional face model corresponding to a user picture, and selecting a sample oral cavity model in an oral cavity model library for the three-dimensional face model; registering the sample oral cavity model into the three-dimensional face model by using an oral cavity position parameter in the three-dimensional face model; performing form adjustment on an oral cavity form of the registered sample oral cavity model by using an expression parameter of the three-dimensional face model to generate a target oral cavity model; and generating, based on the three-dimensional face model and the target oral cavity model, a three-dimensional face expression model corresponding to the user picture.

Abstract: This application provides a method for generating a high-resolution picture performed by a computer device. The method includes: acquiring at least one deep neural network model; acquiring a low-resolution picture; determining a corresponding deep neural network model according to the low-resolution picture; and converting the low-resolution pictures into a high-resolution picture through the deep neural network model, the deep neural network model including a plurality of non-linear conversion convolution layers that alternately use different parameter matrices as convolution template parameters.