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1.发明公开公开(公告)号：US20230154111A1
公开(公告)日：2023-05-18
申请号：US17987586
申请日：2022-11-15
发明人： Taras Andreevich KHAKHULIN , Vanessa Valerievna SKLYAROVA , Victor Sergeevich LEMPITSKY , Egor Olegovich ZAKHAROV
CPC分类号： G06T17/205 , G06T9/00 , G06T7/70 , G06V10/761 , G06V10/82 , G06V40/174 , G06T2207/30201
摘要： A method of three-dimensional reconstruction of human heads using a single photo in the form of polygonal mesh, with animation and realistic rendering capabilities for novel head poses is provided. The method includes encoding, by using a first convolutional neural network, a single source image into a neural texture; estimating, by a pre-trained detailed expression capture and animation (DECA) system, a face shape, a facial expression, and a head pose by using the single source image and a target image, and providing an initial mesh; providing a predicted mesh of a head mesh based on the initial mesh and the neural texture; rendering a human image by using the predicted mesh.
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2.发明申请公开(公告)号：US20230123532A1
公开(公告)日：2023-04-20
申请号：US18083354
申请日：2022-12-16
发明人： Gleb STERKIN , Pavel Ilyich SOLOVEV , Denis Mikhaylovich KORZHENKOV , Victor Sergeevich LEMPITSKY , Taras Andreevich KHAKHULIN
摘要： The present disclosure relates to the field of artificial intelligence (AI) and neural rendering, and particularly to a method of generating a multi-layer representation of a scene using neural networks trained in an end-to-end fashion and to a computing device implementing the method. The method of generating a multi-layer representation of a scene includes: obtaining a pair of images of the scene, the pair of the images comprising a reference image and a source image; performing a reprojection operation on the pair of images to generate a plane-sweep volume; predicting, using a geometry network, a layered structure of the scene based on the plane-sweep volume; and estimating, using a coloring network, color values and opacity values for the predicted layered structure of the scene to obtain the multi-layer representation of the scene; wherein the geometry network and the coloring network are trained in end-to-end manner.
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公开(公告)号：US20220270304A1
公开(公告)日：2022-08-25
申请号：US17741959
申请日：2022-05-11
发明人： Gleb Mikhailovich STERKIN , Ivan Aleksandrovich ANOKHIN , Taras Andreevich KHAKHULIN , Aleksei Vladislavovich KHARLAMOV , Denis Mikhailovich KORZHENKOV , Victor Sergeevich LEMPITSKY , Sergey Igorevich NIKOLENKO , Aleksei Sergeevich SILVESTROV , Pavel Ilich SOLOVEV
摘要： The disclosure relates to a field of plausible timelapse image(s) generation from a single image. A method of generating one or more images of a plausible dayscale timelapse sequence based on a content image using a trained generative neural network and a trained merging neural network is provided. The method includes receiving the content image and one of one or more predefined styles respectively corresponding to times of day to be applied to the content image or style images having styles to be applied to the content image, slicing the content image into n image crops, applying the trained generative neural network with each style to n image crops to obtain n image crops re-stylized according to each style, and merging the re-stylized n image crops for each style with the trained merging neural network to obtain images of a plausible dayscale timelapse sequence for the content image.
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公开(公告)号：US20220207646A1
公开(公告)日：2022-06-30
申请号：US17697436
申请日：2022-03-17
发明人： Ivan Aleksandrovich ANOKHIN , Kirill Vladislavovich DEMOCHKIN , Taras Andreevich KHAKHULIN , Gleb Mikhailovich STERKIN , Victor Sergeevich LEMPITSKY , Denis Mikhailovich KORZHENKOV
摘要： The disclosure relates to multi-layer perceptron architecture, that may be used for image generation. A new architecture for image generators, where the color value at each pixel is computed independently given the value of a random latent vector and the coordinate of that pixel is provided. No spatial convolutions or similar operations that propagate information across pixels are involved during the synthesis.
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