Abstract: A system and a method for controlling the robot. The method includes determining, based on feedback data received from the robot, first position information of a joint of the robot. The feedback data indicates a movement of the joint. The method also includes determining second position information of the joint based on sensing data received from a sensor. The sensing data indicates a relative movement between the joint and a second object to be aligned with the first object. The method also predicts a target position for aligning the first object with the second object.

Abstract: A system and a method for controlling the robot. The method includes determining, based on feedback data received from the robot, first position information of a joint of the robot. The feedback data indicates a movement of the joint. The method also includes determining second position information of the joint based on sensing data received from a sensor. The sensing data indicates a relative movement between the joint and a second object to be aligned with the first object. The method also predicts a target position for aligning the first object with the second object.

Abstract: An image style transform method includes: acquiring an initial image to be subjected to style transform; inputting a gradient of the initial image to an image style transform model, and obtaining a feature map of the initial image in a gradient domain from the image style transform model, where the image style transform model is obtained by being trained in the gradient domain based on a pixel-wise loss and a perceptual loss; and performing image reconstruction according to the feature map of the initial image in the gradient domain to obtain a style image.

Abstract: An image processing method and apparatus, and a computer-readable storage medium are provided. The method includes: determining a target filter kernel; performing, according to the target filter kernel and depth of field information corresponding to an input image, filtering and distinguishing processing on the input image to obtain a non-background area and a blurred background area of the input image; and blending the non-background area and the blurred background area to obtain a background-blurred image.

Abstract: A method for training an image generation network, an electronic device and a storage medium are provided. The method includes: obtaining a sample image, where the sample image includes a first sample image and a second sample image corresponding to the first sample image; processing the first sample image based on an image generation network to obtain a predicted target image; determining a difference loss between the predicted target image and the second sample image; and training the image generation network based on the difference loss to obtain a trained image generation network.

Abstract: An image style transform method includes: acquiring an initial image to be subjected to style transform; inputting a gradient of the initial image to an image style transform model, and obtaining a feature map of the initial image in a gradient domain from the image style transform model, where the image style transform model is obtained by being trained in the gradient domain based on a pixel-wise loss and a perceptual loss; and performing image reconstruction according to the feature map of the initial image in the gradient domain to obtain a style image.