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1.发明申请公开(公告)号：US20200372284A1
公开(公告)日：2020-11-26
申请号：US16616235
申请日：2019-10-16
申请人： Google LLC
发明人： Christoph Rhemann , Abhimitra Meka , Matthew Whalen , Jessica Lynn Busch , Sofien Bouaziz , Geoffrey Douglas Harvey , Andrea Tagliasacchi , Jonathan Taylor , Paul Debevec , Peter Joseph Denny , Sean Ryan Francesco Fanello , Graham Fyffe , Jason Angelo Dourgarian , Xueming Yu , Adarsh Prakash Murthy Kowdle , Julien Pascal Christophe Valentin , Peter Christopher Lincoln , Rohit Kumar Pandey , Christian Häne , Shahram Izadi
摘要： Methods, systems, and media for relighting images using predicted deep reflectance fields are provided. In some embodiments, the method comprises: identifying a group of training samples, wherein each training sample includes (i) a group of one-light-at-a-time (OLAT) images that have each been captured when one light of a plurality of lights arranged on a lighting structure has been activated, (ii) a group of spherical color gradient images that have each been captured when the plurality of lights arranged on the lighting structure have been activated to each emit a particular color, and (iii) a lighting direction, wherein each image in the group of OLAT images and each of the spherical color gradient images are an image of a subject, and wherein the lighting direction indicates a relative orientation of a light to the subject; training a convolutional neural network using the group of training samples, wherein training the convolutional neural network comprises: for each training iteration in a series of training iterations and for each training sample in the group of training samples: generating an output predicted image, wherein the output predicted image is a representation of the subject associated with the training sample with lighting from the lighting direction associated with the training sample; identifying a ground-truth OLAT image included in the group of OLAT images for the training sample that corresponds to the lighting direction for the training sample; calculating a loss that indicates a perceptual difference between the output predicted image and the identified ground-truth OLAT image; and updating parameters of the convolutional neural network based on the calculated loss; identifying a test sample that includes a second group of spherical color gradient images and a second lighting direction; and generating a relit image of the subject included in each of the second group of spherical color gradient images with lighting from the second lighting direction using the trained convolutional neural network.
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2.发明授权公开(公告)号：US10997457B2
公开(公告)日：2021-05-04
申请号：US16616235
申请日：2019-10-16
申请人： Google LLC
发明人： Christoph Rhemann , Abhimitra Meka , Matthew Whalen , Jessica Lynn Busch , Sofien Bouaziz , Geoffrey Douglas Harvey , Andrea Tagliasacchi , Jonathan Taylor , Paul Debevec , Peter Joseph Denny , Sean Ryan Francesco Fanello , Graham Fyffe , Jason Angelo Dourgarian , Xueming Yu , Adarsh Prakash Murthy Kowdle , Julien Pascal Christophe Valentin , Peter Christopher Lincoln , Rohit Kumar Pandey , Christian Häne , Shahram Izadi
摘要： Methods, systems, and media for relighting images using predicted deep reflectance fields are provided. In some embodiments, the method comprises: identifying a group of training samples, wherein each training sample includes (i) a group of one-light-at-a-time (OLAT) images that have each been captured when one light of a plurality of lights arranged on a lighting structure has been activated, (ii) a group of spherical color gradient images that have each been captured when the plurality of lights arranged on the lighting structure have been activated to each emit a particular color, and (iii) a lighting direction, wherein each image in the group of OLAT images and each of the spherical color gradient images are an image of a subject, and wherein the lighting direction indicates a relative orientation of a light to the subject; training a convolutional neural network using the group of training samples, wherein training the convolutional neural network comprises: for each training iteration in a series of training iterations and for each training sample in the group of training samples: generating an output predicted image, wherein the output predicted image is a representation of the subject associated with the training sample with lighting from the lighting direction associated with the training sample; identifying a ground-truth OLAT image included in the group of OLAT images for the training sample that corresponds to the lighting direction for the training sample; calculating a loss that indicates a perceptual difference between the output predicted image and the identified ground-truth OLAT image; and updating parameters of the convolutional neural network based on the calculated loss; identifying a test sample that includes a second group of spherical color gradient images and a second lighting direction; and generating a relit image of the subject included in each of the second group of spherical color gradient images with lighting from the second lighting direction using the trained convolutional neural network.
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