Fudan University

Chao Wen

Yanwei Fu

Yinda Zhang

Xiangyang Xue

Zhuwen Li

Haitao Lin

Jiashun Wang

Tianyun Zou

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 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

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Shanghai, China

Chao Wen