-
1.发明授权公开(公告)号：US11538216B2
公开(公告)日：2022-12-27
申请号：US16558975
申请日：2019-09-03
申请人： Adobe Inc. , Université Laval
发明人： Kalyan Sunkavalli , Yannick Hold-Geoffroy , Christian Gagne , Marc-Andre Gardner , Jean-Francois Lalonde
摘要： This disclosure relates to methods, non-transitory computer readable media, and systems that can render a virtual object in a digital image by using a source-specific-lighting-estimation-neural network to generate three-dimensional (“3D”) lighting parameters specific to a light source illuminating the digital image. To generate such source-specific-lighting parameters, for instance, the disclosed systems utilize a compact source-specific-lighting-estimation-neural network comprising both common network layers and network layers specific to different lighting parameters. In some embodiments, the disclosed systems further train such a source-specific-lighting-estimation-neural network to accurately estimate spatially varying lighting in a digital image based on comparisons of predicted environment maps from a differentiable-projection layer with ground-truth-environment maps.
-
公开(公告)号：US20210073955A1
公开(公告)日：2021-03-11
申请号：US16564398
申请日：2019-09-09
申请人： ADOBE INC.
发明人： Jinsong Zhang , Kalyan K. Sunkavalli , Yannick Hold-Geoffroy , Sunil Hadap , Jonathan Eisenmann , Jean-Francois Lalonde
IPC分类号： G06T5/00
摘要： Methods and systems are provided for determining high-dynamic range lighting parameters for input low-dynamic range images. A neural network system can be trained to estimate high-dynamic range lighting parameters for input low-dynamic range images. The high-dynamic range lighting parameters can be based on sky color, sky turbidity, sun color, sun shape, and sun position. Such input low-dynamic range images can be low-dynamic range panorama images or low-dynamic range standard images. Such a neural network system can apply the estimates high-dynamic range lighting parameters to objects added to the low-dynamic range images.
-
公开(公告)号：US20230098115A1
公开(公告)日：2023-03-30
申请号：US18062460
申请日：2022-12-06
申请人： Adobe Inc. , Université Laval
发明人： Kalyan Sunkavalli , Yannick Hold-Geoffroy , Christian Gagne , Marc-Andre Gardner , Jean-Francois Lalonde
摘要： This disclosure relates to methods, non-transitory computer readable media, and systems that can render a virtual object in a digital image by using a source-specific-lighting-estimation-neural network to generate three-dimensional (“3D”) lighting parameters specific to a light source illuminating the digital image. To generate such source-specific-lighting parameters, for instance, the disclosed systems utilize a compact source-specific-lighting-estimation-neural network comprising both common network layers and network layers specific to different lighting parameters. In some embodiments, the disclosed systems further train such a source-specific-lighting-estimation-neural network to accurately estimate spatially varying lighting in a digital image based on comparisons of predicted environment maps from a differentiable-projection layer with ground-truth-environment maps.
-
4.发明授权公开(公告)号：US11157773B2
公开(公告)日：2021-10-26
申请号：US16802243
申请日：2020-02-26
申请人： ADOBE INC.
摘要： Images can be edited to include features similar to a different target image. An unconditional generative adversarial network (GAN) is employed to edit features of an initial image based on a constraint determined from a target image. The constraint used by the GAN is determined from keypoints or segmentation masks of the target image, and edits are made to features of the initial image based on keypoints or segmentation masks of the initial image corresponding to those of the constraint from the target image. The GAN modifies the initial image based on a loss function having a variable for the constraint. The result of this optimization process is a modified initial image having features similar to the target image subject to the constraint determined from the identified keypoints or segmentation masks.
-
公开(公告)号：US20210319532A1
公开(公告)日：2021-10-14
申请号：US16848741
申请日：2020-04-14
申请人： Adobe Inc.
发明人： Julia Gong , Yannick Hold-Geoffroy , Jingwan Lu
摘要： Techniques and systems are provided for configuring neural networks to perform warping of an object represented in an image to create a caricature of the object. For instance, in response to obtaining an image of an object, a warped image generator generates a warping field using the image as input. The warping field is generated using a model trained with pairings of training images and known warped images using supervised learning techniques and one or more losses. The warped image generator determines, based on the warping field, a set of displacements associated with pixels of the input image. These displacements indicate pixel displacement directions for the pixels of the input image. These displacements are applied to the digital image to generate a warped image of the object.
-
公开(公告)号：US10979640B2
公开(公告)日：2021-04-13
申请号：US16789195
申请日：2020-02-12
申请人： Adobe Inc.
IPC分类号： G06T5/00 , H04N5/232 , G06K9/46 , G06T15/50 , G06N3/08 , H04N5/235 , G06K9/00 , G06K9/62 , G06N3/04
摘要： The present disclosure is directed toward systems and methods for predicting lighting conditions. In particular, the systems and methods described herein analyze a single low-dynamic range digital image to estimate a set of high-dynamic range lighting conditions associated with the single low-dynamic range lighting digital image. Additionally, the systems and methods described herein train a convolutional neural network to extrapolate lighting conditions from a digital image. The systems and methods also augment low-dynamic range information from the single low-dynamic range digital image by using a sky model algorithm to predict high-dynamic range lighting conditions.
-
公开(公告)号：US10957026B1
公开(公告)日：2021-03-23
申请号：US16564398
申请日：2019-09-09
申请人： ADOBE INC.
发明人： Jinsong Zhang , Kalyan K. Sunkavalli , Yannick Hold-Geoffroy , Sunil Hadap , Jonathan Eisenmann , Jean-Francois Lalonde
摘要： Methods and systems are provided for determining high-dynamic range lighting parameters for input low-dynamic range images. A neural network system can be trained to estimate high-dynamic range lighting parameters for input low-dynamic range images. The high-dynamic range lighting parameters can be based on sky color, sky turbidity, sun color, sun shape, and sun position. Such input low-dynamic range images can be low-dynamic range panorama images or low-dynamic range standard images. Such a neural network system can apply the estimates high-dynamic range lighting parameters to objects added to the low-dynamic range images.
-
公开(公告)号：US11488342B1
公开(公告)日：2022-11-01
申请号：US17332708
申请日：2021-05-27
申请人： ADOBE INC.
发明人： Kalyan Krishna Sunkavalli , Yannick Hold-Geoffroy , Milos Hasan , Zexiang Xu , Yu-Ying Yeh , Stefano Corazza
摘要： Embodiments of the technology described herein, make unknown material-maps in a Physically Based Rendering (PBR) asset usable through an identification process that relies, at least in part, on image analysis. In addition, when a desired material-map type is completely missing from a PBR asset the technology described herein may generate a suitable synthetic material map for use in rendering. In one aspect, the correct map type is assigned using a machine classifier, such as a convolutional neural network, which analyzes image content of the unknown material map and produce a classification. The technology described herein also correlates material maps into material definitions using a combination of the material-map type and similarity analysis. The technology described herein may generate synthetic maps to be used in place of the missing material maps. The synthetic maps may be generated using a Generative Adversarial Network (GAN).
-
公开(公告)号：US20220335636A1
公开(公告)日：2022-10-20
申请号：US17231833
申请日：2021-04-15
申请人： ADOBE INC.
发明人： Sai Bi , Zexiang Xu , Kalyan Krishna Sunkavalli , Milos Hasan , Yannick Hold-Geoffroy , David Jay Kriegman , Ravi Ramamoorthi
摘要： A scene reconstruction system renders images of a scene with high-quality geometry and appearance and supports view synthesis, relighting, and scene editing. Given a set of input images of a scene, the scene reconstruction system trains a network to learn a volume representation of the scene that includes separate geometry and reflectance parameters. Using the volume representation, the scene reconstruction system can render images of the scene under arbitrary viewing (view synthesis) and lighting (relighting) locations. Additionally, the scene reconstruction system can render images that change the reflectance of objects in the scene (scene editing).
-
公开(公告)号：US11328385B2
公开(公告)日：2022-05-10
申请号：US16848741
申请日：2020-04-14
申请人： Adobe Inc.
发明人： Julia Gong , Yannick Hold-Geoffroy , Jingwan Lu
摘要： Techniques and systems are provided for configuring neural networks to perform warping of an object represented in an image to create a caricature of the object. For instance, in response to obtaining an image of an object, a warped image generator generates a warping field using the image as input. The warping field is generated using a model trained with pairings of training images and known warped images using supervised learning techniques and one or more losses. The warped image generator determines, based on the warping field, a set of displacements associated with pixels of the input image. These displacements indicate pixel displacement directions for the pixels of the input image. These displacements are applied to the digital image to generate a warped image of the object.
-
-
-
-
-
-
-
-
-
搜索结果
22 条记录 (耗时 0.038 秒)