Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.