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1. 发明专利

GB2593441A Three-dimensional facial reconstruction 未知
公开(公告)号：GB2593441A
公开(公告)日：2021-09-29
申请号：GB202002449
申请日：2020-02-21
申请人： HUAWEI TECH CO LTD
发明人： STEFANOS ZAFEIRIOU , ALEXANDROS LATTAS , STYLIANOS MOSCHOGLOU , STYLIANOS PLOUMPIS , BARIS GECER
IPC分类号： G06T15/04 , G06N3/02 , G06T13/40 , G06T17/00
摘要： Method of generating a 3D facial render from a 2D facial image, comprising: using fitting neural networks to generate from the 2D facial image a 3D facial image shape model and a low resolution 2D facial image texture map; applying a super-resolution model to the low resolution 2D texture map to generate a high resolution 2D texture map; generating a 2D diffuse albedo map from the high resolution texture map using a de-lighting image-to-image translation neural network; rendering a high resolution 3D facial image model using the 2D diffuse albedo map and the 3D facial image shape model. A specular albedo map may also be generated. A 2D normal map of the facial image in object space may be generated and high pas filtered to create a normal map in tangent space. Input maps to the neural networks may be divided into overlapping patches and output patches may be combined. The neural networks may be generative adversarial networks. The super-resolution model may be a convolutional neural network. The 3D shape model may be a combined face and head model. The 2D maps may be UV maps. May facilitate photorealistic rendering of human skin by modelling diffuse reflectance.

2. 发明专利

GB2582010B Combining three-dimensional morphable models 未知
公开(公告)号：GB2582010B
公开(公告)日：2021-07-28
申请号：GB201903125
申请日：2019-03-08
申请人： HUAWEI TECH CO LTD
发明人： STYLIANOS PLOUMPIS , STEFANOS ZAFEIRIOU
IPC分类号： G06T19/20
摘要： A three-dimensional morphable model, (3DMM) is produced by combining first and second 3DMMs, by generating (2.1) a plurality of first shapes using the first 3DMM, and calculating (2.2) a mapping from second parameters of the second 3DMM to first parameters of the first 3DMM. For each second shape generated using the second 3DMM, a corresponding first shape is generated (2.3). Merged shapes are formed (2.4) by merging each second shape with the corresponding first shape. Principal component analysis is performed (2.5) on the merged shapes to generate the new 3DMM. Also claimed is generating a Gaussian process morphable model (GPMM) by combining first and second 3DMMs (see fig. 4) by registering a mean shape of the first 3DMM to a mean shape of the second 3DMM and a template shape, and projecting points of the template shape onto the mean shapes of the first and/or second 3DMM. A universal covariance matrix for the GPMM is determined based on pairs of points of the template shape projected onto the mean shape of the first and/or second 3DMM, a covariance matrix for each of the first and second 3DMMs. The GPMM is defined based on the universal covariance matrix and a predefined mean deformation.

3. 发明专利

GB2581991B Enhancement of three-dimensional facial scans 未知
公开(公告)号：GB2581991B
公开(公告)日：2022-06-01
申请号：GB201903017
申请日：2019-03-06
申请人： HUAWEI TECH CO LTD
发明人： STYLIANOS MOSCHOGLOU , STYLIANOS PLOUMPIS , STEFANOS ZAFEIRIOU
IPC分类号： G06T5/00 , G06N3/08 , G06T1/40
摘要： A method includes applying a generator neural network to a low quality spatial UV map to generate a candidate high quality spatial UV map, applying a discriminator neural network to the candidate high quality spatial UV map to generate a reconstructed candidate high quality spatial UV map, applying the discriminator neural network to a high quality ground truth spatial UV map to generate a reconstructed high quality ground truth spatial UV map, updating parameters of the generator neural network based on a comparison of the candidate high quality spatial UV map and the reconstructed candidate high quality spatial UV map, and updating parameters of the discriminator neural network.

4. 发明专利

GB2582047B Combining three-dimensional morphable models 未知
公开(公告)号：GB2582047B
公开(公告)日：2021-03-31
申请号：GB201917826
申请日：2019-03-08
申请人： HUAWEI TECH CO LTD
发明人： STYLIANOS PLOUMPIS , STEFANOS ZAFEIRIOU
IPC分类号： G06T19/20
摘要： A Gaussian process morphable model (GPMM) is generated by combining first and second 3DMMs (see fig. 4) by registering a mean shape of the first 3DMM to a mean shape of the second 3DMM and a template shape, and projecting template shape points onto the mean shapes of the first and/or second 3DMM. A universal covariance matrix for the GPMM is determined based on pairs of template shape points projected onto the mean shape of the first and/or second 3DMM, and a covariance matrix for each of the first and second 3DMMs. The GPMM is defined based on the universal covariance matrix and a predefined mean deformation. Also disclosed is producing a three-dimensional morphable model, (3DMM) by combining first and second 3DMMs, by generating (2.1) a plurality of first shapes using the first 3DMM, and calculating (2.2) a mapping from second parameters of the second 3DMM to first parameters of the first 3DMM. For each second shape generated using the second 3DMM, a corresponding first shape is generated (2.3). Merged shapes are formed (2.4) by merging each second shape with the corresponding first shape. Principal component analysis is performed (2.5) on the merged shapes to generate the new 3DMM.

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