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SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator
Shunwang Gong, Lei Chen, Michael Bronstein +1
Intrinsic graph convolution operators with differentiable kernel functions play a crucial role in analyzing 3D shape meshes. In this paper, we present a fast and efficient intrinsi…
Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation
Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis +2
Generative models for 3D geometric data arise in many important applications in 3D computer vision and graphics. In this paper, we focus on 3D deformable shapes that share a common…
Single Image 3D Hand Reconstruction with Mesh Convolutions
Dominik Kulon, Haoyang Wang, Riza Alp Güler +2
Monocular 3D reconstruction of deformable objects, such as human body parts, has been typically approached by predicting parameters of heavyweight linear models. In this paper, we…
MeshGAN: Non-linear 3D Morphable Models of Faces
Shiyang Cheng, Michael Bronstein, Yuxiang Zhou +3
Generative Adversarial Networks (GANs) are currently the method of choice for generating visual data. Certain GAN architectures and training methods have demonstrated exceptional p…