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公开(公告)号：US20220101476A1
公开(公告)日：2022-03-31
申请号：US17034467
申请日：2020-09-28
Applicant: Adobe Inc.
Inventor： Oliver Wang , Jianming Zhang , Dingzeyu Li , Zekun Hao
Abstract: The technology described herein is directed to a cross-domain training framework that iteratively trains a domain adaptive refinement agent to refine low quality real-world image acquisition data, e.g., depth maps, when accompanied by corresponding conditional data from other modalities, such as the underlying images or video from which the image acquisition data is computed. The cross-domain training framework includes a shared cross-domain encoder and two conditional decoder branch networks, e.g., a synthetic conditional depth prediction branch network and a real conditional depth prediction branch network. The shared cross-domain encoder converts synthetic and real-world image acquisition data into synthetic and real compact feature representations, respectively. The synthetic and real conditional decoder branch networks convert the respective synthetic and real compact feature representations back to synthetic and real image acquisition data (refined versions) conditioned on data from the other modalities. The cross-domain training framework iteratively trains the domain adaptive refinement agent.
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公开(公告)号：US20210358177A1
公开(公告)日：2021-11-18
申请号：US16874399
申请日：2020-05-14
Applicant: Adobe Inc.
Inventor： Taesung Park , Richard Zhang , Oliver Wang , Junyan Zhu , Jingwan Lu , Elya Shechtman , Alexei A Efros
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a modified digital image from extracted spatial and global codes. For example, the disclosed systems can utilize a global and spatial autoencoder to extract spatial codes and global codes from digital images. The disclosed systems can further utilize the global and spatial autoencoder to generate a modified digital image by combining extracted spatial and global codes in various ways for various applications such as style swapping, style blending, and attribute editing.
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公开(公告)号：US11178368B2
公开(公告)日：2021-11-16
申请号：US16696160
申请日：2019-11-26
Applicant: Adobe Inc.
Inventor： Pulkit Gera , Oliver Wang , Kalyan Krishna Sunkavalli , Elya Shechtman , Chetan Nanda
Abstract: Systems and techniques for automatic digital parameter adjustment are described that leverage insights learned from an image set to automatically predict parameter values for an input item of digital visual content. To do so, the automatic digital parameter adjustment techniques described herein captures visual and contextual features of digital visual content to determine balanced visual output in a range of visual scenes and settings. The visual and contextual features of digital visual content are used to train a parameter adjustment model through machine learning techniques that captures feature patterns and interactions. The parameter adjustment model exploits these feature interactions to determine visually pleasing parameter values for an input item of digital visual content. The predicted parameter values are output, allowing further adjustment to the parameter values.
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公开(公告)号：US20210256717A1
公开(公告)日：2021-08-19
申请号：US16790056
申请日：2020-02-13
Applicant: Adobe Inc.
Inventor： Zhe Lin , Oliver Wang , Mai Long , Ke Xian , Jianming Zhang
Abstract: In order to provide monocular depth prediction, a trained neural network may be used. To train the neural network, edge detection on a digital image may be performed to determine at least one edge of the digital image, and then a first point and a second point of the digital image may be sampled, based on the at least one edge. A relative depth between the first point and the second point may be predicted, and the neural network may be trained to perform monocular depth prediction using a loss function that compares the predicted relative depth with a ground truth relative depth between the first point and the second point.
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公开(公告)号：US11049525B2
公开(公告)日：2021-06-29
申请号：US16281903
申请日：2019-02-21
Applicant: Adobe Inc.
Inventor： Bernd Huber , Bryan Russell , Gautham Mysore , Hijung Valentina Shin , Oliver Wang
IPC: G11B27/11
Abstract: Certain embodiments involve transcript-based techniques for facilitating insertion of secondary video content into primary video content. For instance, a video editor presents a video editing interface having a primary video section displaying a primary video, a text-based navigation section having navigable portions of a primary video transcript, and a secondary video menu section displaying candidate secondary videos. In some embodiments, candidate secondary videos are obtained by using target terms detected in the transcript to query a remote data source for the candidate secondary videos. In embodiments involving video insertion, the video editor identifies a portion of the primary video corresponding to a portion of the transcript selected within the text-based navigation section. The video editor inserts a secondary video, which is selected from the candidate secondary videos based on an input received at the secondary video menu section, at the identified portion of the primary video.
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Patent Agency Ranking
公开(公告)号：US20210158570A1
公开(公告)日：2021-05-27
申请号：US16692503
申请日：2019-11-22
Applicant: Adobe Inc.
Inventor： Tharun Mohandoss , Pulkit Gera , Oliver Wang , Kartik Sethi , Kalyan Sunkavalli , Elya Shechtman , Chetan Nanda
Abstract: This disclosure involves training generative adversarial networks to shot-match two unmatched images in a context-sensitive manner. For example, aspects of the present disclosure include accessing a trained generative adversarial network including a trained generator model and a trained discriminator model. A source image and a reference image may be inputted into the generator model to generate a modified source image. The modified source image and the reference image may be inputted into the discriminator model to determine a likelihood that the modified source image is color-matched with the reference image. The modified source image may be outputted as a shot-match with the reference image in response to determining, using the discriminator model, that the modified source image and the reference image are color-matched.
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公开(公告)号：US20210142042A1
公开(公告)日：2021-05-13
申请号：US17154830
申请日：2021-01-21
Applicant: Adobe Inc.
Inventor： Kartik Sethi , Oliver Wang , Tharun Mohandoss , Elya Shechtman , Chetan Nanda
Abstract: In implementations of skin tone assisted digital image color matching, a device implements a color editing system, which includes a facial detection module to detect faces in an input image and in a reference image, and includes a skin tone model to determine a skin tone value reflective of a skin tone of each of the faces. A color matching module can be implemented to group the faces into one or more face groups based on the skin tone value of each of the faces, match a face group pair as an input image face group paired with a reference image face group, and generate a modified image from the input image based on color features of the reference image, the color features including face skin tones of the respective faces in the face group pair as part of the color features applied to modify the input image.
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公开(公告)号：US20210012189A1
公开(公告)日：2021-01-14
申请号：US16507675
申请日：2019-07-10
Applicant: Adobe Inc.
Inventor： Oliver Wang , Kevin Wampler , Kalyan Krishna Sunkavalli , Elya Shechtman , Siddhant Jain
Abstract: Techniques for incorporating a black-box function into a neural network are described. For example, an image editing function may be the black-box function and may be wrapped into a layer of the neural network. A set of parameters and a source image are provided to the black-box function, and the output image that represents the source image with the set of parameters applied to the source image is output from the black-box function. To address the issue that the black-box function may not be differentiable, a loss optimization may calculate the gradients of the function using, for example, a finite differences calculation, and the gradients are used to train the neural network to ensure the output image is representative of an expected ground truth image.
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公开(公告)号：US20200372710A1
公开(公告)日：2020-11-26
申请号：US16985402
申请日：2020-08-05
Applicant: Adobe, Inc.
Inventor： Oliver Wang , Vladimir Kim , Matthew Fisher , Elya Shechtman , Chen-Hsuan Lin , Bryan Russell
Abstract: Techniques are disclosed for 3D object reconstruction using photometric mesh representations. A decoder is pretrained to transform points sampled from 2D patches of representative objects into 3D polygonal meshes. An image frame of the object is fed into an encoder to get an initial latent code vector. For each frame and camera pair from the sequence, a polygonal mesh is rendered at the given viewpoints. The mesh is optimized by creating a virtual viewpoint, rasterized to obtain a depth map. The 3D mesh projections are aligned by projecting the coordinates corresponding to the polygonal face vertices of the rasterized mesh to both selected viewpoints. The photometric error is determined from RGB pixel intensities sampled from both frames. Gradients from the photometric error are backpropagated into the vertices of the assigned polygonal indices by relating the barycentric coordinates of each image to update the latent code vector.
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公开(公告)号：US10740881B2
公开(公告)日：2020-08-11
申请号：US15935994
申请日：2018-03-26
Applicant: Adobe Inc.
Inventor： Oliver Wang , Michal Lukac , Elya Shechtman , Mahyar Najibikohnehshahri
Abstract: Techniques for using deep learning to facilitate patch-based image inpainting are described. In an example, a computer system hosts a neural network trained to generate, from an image, code vectors including features learned by the neural network and descriptive of patches. The image is received and contains a region of interest (e.g., a hole missing content). The computer system inputs it to the network and, in response, receives the code vectors. Each code vector is associated with a pixel in the image. Rather than comparing RGB values between patches, the computer system compares the code vector of a pixel inside the region to code vectors of pixels outside the region to find the best match based on a feature similarity measure (e.g., a cosine similarity). The pixel value of the pixel inside the region is set based on the pixel value of the matched pixel outside this region.
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