An image processing device according to one embodiment estimates optical flow information, pixel by pixel, on the basis of a reference image and input images of consecutive frames, and estimates a term corresponding to temporal consistency between the frames of the input images. The image processing device determines a mesh on the basis of the term corresponding to temporal consistency and the optical flow information, and transforms the reference image on the basis of the mesh. The image processing device preforms image blending on the basis of the input image, the transformed reference image, and mask data.

Techniques are disclosed for training and applying nonlinear face models. In embodiments, a nonlinear face model includes an identity encoder, an expression encoder, and a decoder. The identity encoder takes as input a representation of a facial identity, such as a neutral face mesh minus a reference mesh, and outputs a code associated with the facial identity. The expression encoder takes as input a representation of a target expression, such as a set of blendweight values, and outputs a code associated with the target expression. The codes associated with the facial identity and the facial expression can be concatenated and input into the decoder, which outputs a representation of a face having the facial identity and expression. The representation of the face can include vertex displacements for deforming the reference mesh.

A method comprising: receiving a reference facial image of a first subject, wherein the reference image represents a specified makeup style applied to a face of the first subject; receiving a target facial image of a target subject without makeup; performing pixel-wise alignment of the reference image to the target image; generating a translation of the reference image to obtain a de-makeup version of the reference image representing the face of the first subject without the specified makeup style; calculating an appearance modification contribution representing a difference between the reference image and the de-makeup version; and adding the calculated appearance modification contribution to the target image, to construct a modified the target image which represents the specified makeup style applied to a face of the target subject.

The present application provides a comparison method and a modeling method for a chip product, a device and a storage medium. According to the method, the chip product is modeled by using a neural network based on a slice sequence of the chip product in advance to obtain a three-dimensional stereoscopic model. When the chip products are compared, a comparison feature is acquired responsive to an operation of a user. For each chip product, a comparison result corresponding to the comparison feature is acquired from the three-dimensional stereoscopic model corresponding to each chip product. Then, the comparison result corresponding to each chip product is displayed.

Systems and methods of improving alignment in dense prediction neural networks are disclosed. A method includes identifying, at a computing system, an input data set and a label data set with one or more first parts of the input data set corresponding to a label. The computing system processes the input data set using a neural network to generate a predicted label data set that identifies one or more second parts of the input data set predicted to correspond to the label. The computing system determines an alignment result using the predicted label data set and the label data set and a transformation of the one or more first parts, including a shift, rotation, scaling, and/or deformation, based on the alignment result. The computing system computes a loss score using the transformation, label data and the predicted label data set and updates the neural network based on the loss score.

A positioning method for facial images implemented by a smart mirror having a processor, a display module, and an image capturing module is disclosed and includes following steps: displaying a default positioning mask through the display module after the smart mirror is activated; capturing a facial image of a user by the image capturing module when a real-time image of the user is detected and aligned with the displayed default positioning mask; analyzing the facial image for obtaining multiple facial feature points from the same; creating multiple contour lines based on the multiple facial feature points, wherein the multiple contour lines at least depict facial contours and facial features of the user; establishing a customized positioning mask exclusively belonging to the user according to the multiple contour lines; and, storing the customized positioning mask for substituting the default positioning mask for the user.

Methods, systems, workstations, and computer program products that provide automated implant analysis of batches of image data sets of a plurality of different patients having an implant coupled to bone using a first data set of a first patient from the batch of image data sets, the first data set comprising a first image stack and a second image stack and allowing a user to select parameter settings for implant movement analysis of the implant including selecting a first object of interest and a second reference object. Measurements of movement of the implant and/or coupled bone can be automatically calculated and selected parameter settings can be automatically propagated to other image data sets of other patients of the batch of image data sets and measurements for the batch of image data sets of others of the different patients can be automatically calculated.

Systems and methods are disclosed for modifying labeled target content for a capture device. A computer-implemented method may use a computer system that includes non-transient electronic storage, a graphical user interface, and one or more physical computer processors. The computer-implemented method may include: obtaining labeled target content, the labeled target content including one or more facial features that have been labeled; modifying the labeled target content to match dynamically captured content from a first capture device to generate modified target content; and storing the modified target content. The dynamically captured content may include the one or more facial features.

A computer device extracts a plurality of target windows from a target video. Each of the target windows comprises a respective plurality of consecutive video frames. For each of the target windows, the device performs action recognition on the respective plurality of consecutive video frames corresponding to the target window to obtain respective first action feature information of the target window. The device obtains a similarity between the first action feature information of the target window and preset feature information. The device determines, from the respective obtained similarities corresponding to the plurality of target windows, a highest first similarity and a first target window corresponding to the highest first similarity. The device also determines a dynamic action corresponding to the highest first similarity as the preset dynamic action in accordance with threshold settings.

Methods and systems are provided for electronic authentication. A modified electronic image is generated by altering at least a pixel of an electronic image. The electronic image is an image that has been previously viewed by a user during a setup process. In response to receiving an authentication request from the user, the modified electronic image is displayed to the user via an electronic display along with one or more other electronic images. A determination is made as to whether the user is able to recognize the modified electronic image. In response to determination that the user is able to recognize the modified electronic image, the authenticating request is granted.