A system and method for three-dimensional (3D) microgeometry and reflectance modeling is provided. The system receives images comprising a first set of images of a face and a second set of images of the face. The faces in the first set of images and the second set of images are exposed to omni-directional lighting and directional lighting, respectively. The system generates a 3D face mesh based on the received images and executes a set of skin-reflectance modeling operations by using the generated 3D face mesh and the second set of images, to estimate a set of texture maps for the face. Based on the estimated set of texture maps, the system texturizes the generated 3D face mesh. The texturization includes an operation in which texture information, including microgeometry skin details and skin reflectance details, of the estimated set of texture maps is mapped onto the generated 3D face mesh.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to input a fisheye image to a vector quantized variational autoencoder. The vector quantized variational autoencoder can encode the fisheye image to first latent variables based on an encoder. The vector quantized variational autoencoder can quantize the first latent variables to generate second latent variables based on a dictionary of embeddings. The vector quantized variational autoencoder can decode the second latent variables to a rectified rectilinear image using a decoder and output the rectified rectilinear image.

A method may include obtaining an image of a scene from a first perspective, the image including an object, and detecting the object in the image using a machine learning process, where the object may be representative of a known shape with at least four vertices at a first set of points. The method may also include automatically predicting a second set of points corresponding to the at least four vertices of the object in a second perspective of the scene based on the known shape of the object. The method may additionally include constructing, without user input, a transformation matrix to transform a given image from the first perspective to the second perspective based on the first set of points and the second set of points.

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly restoring degraded digital images utilizing a deep learning framework for repairing local defects, correcting global imperfections, and/or enhancing depicted faces. In particular, the disclosed systems can utilize a defect detection neural network to generate a segmentation map indicating locations of local defects within a digital image. In addition, the disclosed systems can utilize an inpainting algorithm to determine pixels for inpainting the local defects to reduce their appearance. In some embodiments, the disclosed systems utilize a global correction neural network to determine and repair global imperfections. Further, the disclosed systems can enhance one or more faces depicted within a digital image utilizing a face enhancement neural network as well.

An electronic apparatus according to the present invention, includes at least one memory and at least one processor which function as: an acquisition unit configured to acquire a captured image; and a control unit configured to control so as to extract a partial range of an image acquired by the acquisition unit and record a moving image that is not a VR image in a storage, and control so as to record a still image that is a VR image in the storage based on the acquired image.

Systems and methods are disclosed for aligning a two-dimensional (2D) design image to a 2D projection image of a three-dimensional (3D) design model. One method comprises receiving a 2D design document, the 2D design document comprising a 2D design image, and receiving a 3D design file comprising a 3D design model, the 3D design model comprising one or more design elements. The method further comprises generating a 2D projection image based on the 3D design model, the 2D projection image comprising a representation of at least a portion of the one or more design elements, generating a projection barcode based on the 2D projection image, and generating a drawing barcode based on the 2D design image. The method further comprises aligning the 2D projection image and the 2D design image by comparing the projection barcode and the drawing barcode.

This disclosure relates to a device for digital signal processing, particularly video image processing. The device obtains image data comprising a plurality of pixels. The image data comprises a plurality of sequentially captured images. The device estimates, for a target image, a set of backward motion vector fields (backward MVFs) based on the target image, and a first set of images captured before the target image. The device further estimates a set of forward MVFs based on the target image and a second set of images captured after the target image. Depending on the estimating for the target image, the device generates an output image based on a merging procedure of the target image and the first set of images and the set of backward MVFs, and/or the second set of images and the set of forward MVFs.

A method of image retargeting is provided. The method includes obtaining a source image, obtaining a target size for a retargeted image based on the source image, generating a two-dimensional importance map for the source image, generating, based on the two-dimensional importance map and the target size, a warping mesh having a distortion metric below a threshold value, determining whether a size of the warping mesh corresponds to the target size, and based on the size of the warping mesh being determined to correspond to the target size, rendering the retargeted image by applying the warping mesh to the source image.

Arthroscopes and laparoscopes are available in several lens cuts to cover different clinical situations of interest, with the surgeon having to exchange the optics in order to change the direction of view of the camera. The presently disclosed embodiments disclose real-time image processing systems and methods that enable the user to arbitrarily change the direction of view of a surgical camera with the advantage of avoiding the disruption in workflow caused by the physical exchange of the optics. In addition, the presently disclosed embodiments disclose performing zoom along an arbitrary viewing direction (directional zoom) that enables to increase the scale of a region of interest without decreasing the overall field-of-view or losing image contents.

A computer-implemented method of processing video data comprising a sequence of image frames. The method includes isolating an instance of an object within the sequence of image frames, generating a modified instance of the object using a machine learning model, and modifying the video data to smoothly transition between at least part of the isolated instance of the object and a corresponding at least part of the modified instance of the object over a subsequence of the sequence of image frames.