text: 127,122,1224724623910154242,458603378,Marian,"New Paper: Adversarial Generation of Continuous Implicit Shape Representations We propose two GANs with a DeepSDF network as the generator and either a 3D CNN or a Pointnet as the discriminator. Written by @rusty1s and me! Paper: <LINK> <LINK> @BriamMor I started with 3D GANs and only did 2D GANs after that. My advice would be to start with 2D GANs since that makes data preparation and visualization a lot easier. If you want to get results quickly, try to get existing implementations to run. @BriamMor But if you want to understand how it works, try to implement it yourself. I would start with a fully connected GAN on something like MNIST, then a DCGAN, then WGAN-GP, then implement progressive growing, then StyleGAN. You could also go for StyleGAN right away. @rusty1s Check out my blog post about the paper. It has animated visualizations! <LINK> <LINK>",https://arxiv.org/abs/2002.00349,"This work presents a generative adversarial architecture for generating three-dimensional shapes based on signed distance representations. While the deep generation of shapes has been mostly tackled by voxel and surface point cloud approaches, our generator learns to approximate the signed distance for any point in space given prior latent information. Although structurally similar to generative point cloud approaches, this formulation can be evaluated with arbitrary point density during inference, leading to fine-grained details in generated outputs. Furthermore, we study the effects of using either progressively growing voxel- or point-processing networks as discriminators, and propose a refinement scheme to strengthen the generator's capabilities in modeling the zero iso-surface decision boundary of shapes. We train our approach on the ShapeNet benchmark dataset and validate, both quantitatively and qualitatively, its performance in generating realistic 3D shapes. ",Adversarial Generation of Continuous Implicit Shape Representations,4,"['New Paper: Adversarial Generation of Continuous Implicit Shape Representations\n\nWe propose two GANs with a DeepSDF network as the generator and either a 3D CNN or a Pointnet as the discriminator. Written by @rusty1s and me!\n\nPaper: <LINK> <LINK>', '@BriamMor I started with 3D GANs and only did 2D GANs after that. My advice would be to start with 2D GANs since that makes data preparation and visualization a lot easier. If you want to get results quickly, try to get existing implementations to run.', '@BriamMor But if you want to understand how it works, try to implement it yourself. I would start with a fully connected GAN on something like MNIST, then a DCGAN, then WGAN-GP, then implement progressive growing, then StyleGAN. You could also go for StyleGAN right away.', '@rusty1s Check out my blog post about the paper. It has animated visualizations!\nhttps://t.co/wLFpUKgMv7 https://t.co/5QVT7xUx0w']",20,02,863