Patents by Inventor Yanwei Fu
Yanwei Fu has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11593957Abstract: A network for category-level 6D pose and size estimation, including a 3D-OCR module for 3D Orientation-Consistent Representation, a GeoReS module for Geometry-constrained Reflection Symmetry, and a MPDE module for Mirror-Paired Dimensional Estimation; wherein the 3D-OCR module and the GeoReS module are incorporated in parallel; the 3D-OCR module receives a canonical template shape including canonical category-specific keypoints; the GeoReS module receives an original input depth observation including pre-processed predicted category labels and potential masks of the target instances; the MPDE module receives the output from the GeoReS module as well as the original input depth observation; and the network outputs the estimation results based on the output of the MPDE module, the output of the 3D-OCR module, as well as the canonical template shape. Also provided are corresponding systems and methods.Type: GrantFiled: March 10, 2022Date of Patent: February 28, 2023Assignee: Fudan UniversityInventors: Yanwei Fu, Haitao Lin, Xiangyang Xue
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Patent number: 11580622Abstract: A system for image inpainting is provided, including an encoder, a decoder, and a sketch tensor space of a third-order tensor; wherein the encoder includes an improved wireframe parser and a canny detector, and a pyramid structure sub-encoder; the improved wireframe parser is used to extract line maps from an original image input to the encoder, the canny detector is used to extract edge maps from the original image, and the pyramid structure sub-encoder is used to generate the sketch tensor space based on the original image, the line maps and the edge maps; and the decoder outputs an inpainted image from the sketch tensor space. A method thereof is also provided.Type: GrantFiled: March 15, 2022Date of Patent: February 14, 2023Assignee: FUDAN UNIVERSITYInventors: Yanwei Fu, Chenjie Cao
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Patent number: 11455776Abstract: A network for neural pose transfer includes a pose feature extractor, and a style transfer decoder, wherein the pose feature extractor comprises a plurality of sequential extracting stacks, each extracting stack consists of a first convolution layer and an Instance Norm layer sequential to the first convolution layer. The style transfer decoder comprises a plurality of sequential decoding stacks, a second convolution layer sequential to the plurality of decoding stacks and a tan h layer sequential to the second convolution layer. Each decoding stack consists of a third convolution layer and a SPAdaIn residual block. A source pose mesh is input to the pose feature extractor, and an identity mesh is concatenated with the output of the pose feature extractor and meanwhile fed to each SPAdaIn residual block of the style transfer decoder. A system thereof is also provided.Type: GrantFiled: September 10, 2020Date of Patent: September 27, 2022Assignee: FUDAN UNIVERSITYInventors: Yanwei Fu, Xiangyang Xue, Yinda Zhang, Chao Wen, Haitao Lin, Jiashun Wang, Tianyun Zou
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Publication number: 20220292698Abstract: A network for category-level 6D pose and size estimation, including a 3D-OCR module for 3D Orientation-Consistent Representation, a GeoReS module for Geometry-constrained Reflection Symmetry, and a MPDE module for Mirror-Paired Dimensional Estimation; wherein the 3D-OCR module and the GeoReS module are incorporated in parallel; the 3D-OCR module receives a canonical template shape including canonical category-specific keypoints; the GeoReS module receives an original input depth observation including pre-processed predicted category labels and potential masks of the target instances; the MPDE module receives the output from the GeoReS module as well as the original input depth observation; and the network outputs the estimation results based on the output of the MPDE module, the output of the 3D-OCR module, as well as the canonical template shape. Also provided are corresponding systems and methods.Type: ApplicationFiled: March 10, 2022Publication date: September 15, 2022Inventors: Yanwei Fu, Haitao Lin, Xiangyang Xue
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Publication number: 20220292651Abstract: A system for image inpainting is provided, including an encoder, a decoder, and a sketch tensor space of a third-order tensor; wherein the encoder includes an improved wireframe parser and a canny detector, and a pyramid structure sub-encoder; the improved wireframe parser is used to extract line maps from an original image input to the encoder, the canny detector is used to extract edge maps from the original image, and the pyramid structure sub-encoder is used to generate the sketch tensor space based on the original image, the line maps and the edge maps; and the decoder outputs an inpainted image from the sketch tensor space. A method thereof is also provided.Type: ApplicationFiled: March 15, 2022Publication date: September 15, 2022Inventors: Yanwei Fu, Chenjie Cao
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Publication number: 20210407200Abstract: A network for neural pose transfer includes a pose feature extractor, and a style transfer decoder, wherein the pose feature extractor comprises a plurality of sequential extracting stacks, each extracting stack consists of a first convolution layer and an Instance Norm layer sequential to the first convolution layer. The style transfer decoder comprises a plurality of sequential decoding stacks, a second convolution layer sequential to the plurality of decoding stacks and a tan h layer sequential to the second convolution layer. Each decoding stack consists of a third convolution layer and a SPAdaIn residual block. A source pose mesh is input to the pose feature extractor, and an identity mesh is concatenated with the output of the pose feature extractor and meanwhile fed to each SPAdaIn residual block of the style transfer decoder. A system thereof is also provided.Type: ApplicationFiled: September 10, 2020Publication date: December 30, 2021Inventors: Yanwei Fu, Xiangyang Xue, Yinda Zhang
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Patent number: 11055549Abstract: A network for image processing is provided, and more particularly, for coarse-to-fine recognition of image processing.Type: GrantFiled: May 20, 2019Date of Patent: July 6, 2021Assignee: FUDAN UNIVERSITYInventors: Yugang Jiang, Yanwei Fu, Changmao Cheng, Xiangyang Xue
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Publication number: 20210027536Abstract: A network for generating 3D shape includes a perceptual network and a Graphic Convolutional Network (GCN). The GCN includes a coarse shape generation network for generating a coarse shape, and a Multi-View Deformation Network (MDN) for refining the coarse shape. The MDN further comprises at least one MDN unit, which in turn comprises a deformation hypothesis sampling module, a cross-view perceptual feature pooling module and a deformation reasoning module. Systems and methods are also provided.Type: ApplicationFiled: May 23, 2020Publication date: January 28, 2021Inventors: Yanwei Fu, Chao Wen, Yinda Zhang, Zhuwen Li
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Patent number: 10885707Abstract: A network for generating 3D shape includes a perceptual network and a Graphic Convolutional Network (GCN). The GCN includes a coarse shape generation network for generating a coarse shape, and a Multi-View Deformation Network (MDN) for refining the coarse shape. The MDN further comprises at least one MDN unit, which in turn comprises a deformation hypothesis sampling module, a cross-view perceptual feature pooling module and a deformation reasoning module. Systems and methods are also provided.Type: GrantFiled: May 23, 2020Date of Patent: January 5, 2021Assignee: FUDAN UNIVERSITYInventors: Yanwei Fu, Chao Wen, Yinda Zhang, Zhuwen Li
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Patent number: 10783709Abstract: This invention is related to a network for generating 3D shape, including an image feature network, an initial ellipsoid mesh, and a cascaded mesh deformation network. The image feature network is a Visual Geometry Group Net (VGGN) containing five successive convolutional layer groups, and four pooling layers sandwiched by the five convolutional layer groups; and the cascaded mesh deformation network is a graph-based convolution network (GCN) containing three successive deformation blocks, and two graph unpooling layers sandwiched by the three successive deformation blocks. This invention is also related to a system and a method thereof.Type: GrantFiled: November 12, 2019Date of Patent: September 22, 2020Assignee: FUDAN UNIVERSITYInventors: Yugang Jiang, Yanwei Fu, Nanyang Wang, Yinda Zhang, Zhuwen Li
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Patent number: 10777003Abstract: This invention is related to a network for generating 3D shape, including an image feature network, an initial ellipsoid mesh, and a cascaded mesh deformation network. The image feature network is a Visual Geometry Group Net (VGGN) containing five successive convolutional layer groups, and four pooling layers sandwiched by the five convolutional layer groups; and the cascaded mesh deformation network is a graph-based convolution network (GCN) containing three successive deformation blocks, and two graph unpooling layers sandwiched by the three successive deformation blocks. This invention is also related to a system and a method thereof.Type: GrantFiled: July 23, 2019Date of Patent: September 15, 2020Assignee: FUDAN UNIVERSITYInventors: Yugang Jiang, Yanwei Fu, Nanyang Wang, Yinda Zhang, Zhuwen Li
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Patent number: 10614379Abstract: Techniques are disclosed for identifying and filtering outliers from a sample set of data prior to training a classifier on an object using the sample set. A data set including a plurality of samples used to train a classification model is retrieved. The samples in the data set have a feature dimensionality. A graph of the data set is built. Each node in the graph corresponds to a sample in the data set and edges connecting the nodes correspond to a measure of similarity between the nodes. The feature dimensionality of the sample data set is reduced based on a topology of the graph. One or more outliers in the data set are identified based on the reduced feature dimensionality.Type: GrantFiled: September 27, 2016Date of Patent: April 7, 2020Assignee: Disney Enterprises, Inc.Inventors: Yanwei Fu, Leonid Sigal
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Publication number: 20200082620Abstract: This invention is related to a network for generating 3D shape, including an image feature network, an initial ellipsoid mesh, and a cascaded mesh deformation network. The image feature network is a Visual Geometry Group Net (VGGN) containing five successive convolutional layer groups, and four pooling layers sandwiched by the five convolutional layer groups; and the cascaded mesh deformation network is a graph-based convolution network (GCN) containing three successive deformation blocks, and two graph unpooling layers sandwiched by the three successive deformation blocks. This invention is also related to a system and a method thereof.Type: ApplicationFiled: November 12, 2019Publication date: March 12, 2020Inventors: Yugang Jiang, Yanwei Fu, Nanyang Wang, Yinda Zhang, Zhuwen Li
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Publication number: 20200027269Abstract: This invention is related to a network for generating 3D shape, including an image feature network, an initial ellipsoid mesh, and a cascaded mesh deformation network. The image feature network is a Visual Geometry Group Net (VGGN) containing five successive convolutional layer groups, and four pooling layers sandwiched by the five convolutional layer groups; and the cascaded mesh deformation network is a graph-based convolution network (GCN) containing three successive deformation blocks, and two graph unpooling layers sandwiched by the three successive deformation blocks. This invention is also related to a system and a method thereof.Type: ApplicationFiled: July 23, 2019Publication date: January 23, 2020Inventors: Yugang Jiang, Yanwei Fu, Nanyang Wang, Yinda Zhang, Zhuwen Li
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Publication number: 20200026942Abstract: A network for image processing is provided, and more particularly, for coarse-to-fine recognition of image processing.Type: ApplicationFiled: May 20, 2019Publication date: January 23, 2020Inventors: Yugang Jiang, Yanwei Fu, Changmao Cheng, Xiangyang Xue
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Patent number: 10482329Abstract: There is provided a system including a non-transitory memory storing an executable code and a hardware processor executing the executable code to receive a plurality of training contents depicting a plurality of activities, extract training object data from the plurality of training contents including a first training object data corresponding to a first activity, extract training scene data from the plurality of training contents including a first training scene data corresponding to the first activity, determine that a probability of the first activity is maximized when the first training object data and the first training scene data both exist in a sample media content.Type: GrantFiled: February 28, 2018Date of Patent: November 19, 2019Assignee: Disney Enterprises, Inc.Inventors: Zuxuan Wu, Yanwei Fu, Leonid Sigal
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Patent number: 10331676Abstract: Items of interest within digital information may be detected and associated with a label that provides context to the item of interest. The label may describe an item category of the item of interest. The knowledge base of item categories may be limited. Additional item categories may be learned by accessing sets of vocabulary that may relate to the known item categories.Type: GrantFiled: April 13, 2016Date of Patent: June 25, 2019Assignee: Disney Enterprises, Inc.Inventors: Yanwei Fu, Leonid Sigal
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Publication number: 20180189569Abstract: There is provided a system including a non-transitory memory storing an executable code and a hardware processor executing the executable code to receive a plurality of training contents depicting a plurality of activities, extract training object data from the plurality of training contents including a first training object data corresponding to a first activity, extract training scene data from the plurality of training contents including a first training scene data corresponding to the first activity, determine that a probability of the first activity is maximized when the first training object data and the first training scene data both exist in a sample media content.Type: ApplicationFiled: February 28, 2018Publication date: July 5, 2018Inventors: Zuxuan Wu, Yanwei Fu, Leonid Sigal
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Patent number: 9940522Abstract: There is provided a system including a non-transitory memory storing an executable code and a hardware processor executing the executable code to receive a plurality of training contents depicting a plurality of activities, extract training object data from the plurality of training contents including a first training object data corresponding to a first activity, extract training scene data from the plurality of training contents including a first training scene data corresponding to the first activity, determine that a probability of the first activity is maximized when the first training object data and the first training scene data both exist in a sample media content.Type: GrantFiled: July 15, 2016Date of Patent: April 10, 2018Assignee: Disney Enterprises, Inc.Inventors: Zuxuan Wu, Yanwei Fu, Leonid Sigal
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Publication number: 20180089580Abstract: Techniques are disclosed for identifying and filtering outliers from a sample set of data prior to training a classifier on an object using the sample set. A data set including a plurality of samples used to train a classification model is retrieved. The samples in the data set have a feature dimensionality. A graph of the data set is built. Each node in the graph corresponds to a sample in the data set and edges connecting the nodes correspond to a measure of similarity between the nodes. The feature dimensionality of the sample data set is reduced based on a topology of the graph. One or more outliers in the data set are identified based on the reduced feature dimensionality.Type: ApplicationFiled: September 27, 2016Publication date: March 29, 2018Inventors: Yanwei FU, Leonid SIGAL