Fudan University

Yanwei Fu

Yinda Zhang

Yugang Jiang

Xiangyang Xue

Zhuwen Li

Haitao Lin

Chao Wen

Nanyang Wang

Jiashun Wang

Changmao Cheng

Tianyun Zou

Chenjie Cao

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.

Network and system for pose and size estimation

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.

System and method for image inpainting

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.

Network and system for neural pose transfer

A network for image processing is provided, and more particularly, for coarse-to-fine recognition of image processing. The network includes a shared convolution layer, and a first subnet and a second subnet both subsequent to the shared convolution layer; the first subnet comprises a first skipping module comprising one or more skip-dense blocks iteratively stacked with one or more transition layers, a first pooling layer subsequent to the first skipping module, and a first classification layer subsequent to the first pooling layer; the second subnet comprises a second skipping module comprising one or more skip-dense blocks iteratively stacked with one or more layers, a second pooling layer subsequent to the second skipping module, and a second classification layer subsequent to the second pooling layer; and wherein a skip-dense block of the second subnet is selected to guide a transition layer of the first subnet, and the level of the guiding skip-dense block is deeper than the level of the guided transition layer. This network is also related to a system and a method thereof.

Network, system and method for image processing

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.

Network, system and method for multi-view 3D mesh generation via deformation

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.

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Shanghai, China

Yanwei Fu