In this paper we introduced a representation learning and generative framework for fixed topology 3D deformable shapes, by using a mesh convolutional operator, spiral convolutions, that efficiently encodes the inductive bias of the fixed topology
We propose a novel AutoEncoder framework to explicitly disentangle pose and appearance features from RGB imagery and the long short-term memory-based integration of pose features over time produces the gait feature
We leverage two type of free geometry data: optical flow from synthesis image and disparity map from real 3D movies. These cues effectively drive the convolutional neural networks to extract generic knowledge from the conventional videos that is useful for the high-level semantic...
We proposed a novel architecture combining a Variational Auto-Encoder and a Generative Adversarial Network to create an identity-invariant representation of a face image that permits synthesis of an expression-preserving and realistic version
Extensive experiments demonstrate that the latent feature representations learned by metapath2vec and metapath2vec++ are able to improve various heterogeneous network mining tasks, such as similarity search, node classi cation, and clustering