8 citations · 8 across the 3 of their papers we have counts for
6 papers · 1 filter
PixARMesh: Autoregressive Mesh-Native Single-View Scene Reconstruction
Xiang Zhang, Sohyun Yoo, Hongrui Wu +3
We introduce PixARMesh, a method to autoregressively reconstruct complete 3D indoor scene meshes directly from a single RGB image. Unlike prior methods that rely on implicit signed…
clip2latent: Text driven sampling of a pre-trained StyleGAN using denoising diffusion and CLIP
Justin N. M. Pinkney, Chuan Li
We introduce a new method to efficiently create text-to-image models from a pre-trained CLIP and StyleGAN. It enables text driven sampling with an existing generative model without…
NPRportrait 1.0: A Three-Level Benchmark for Non-Photorealistic Rendering of Portraits
Paul L. Rosin, Yu-Kun Lai, David Mould +10
Despite the recent upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer,…
HoloGAN: Unsupervised learning of 3D representations from natural images
Thu Nguyen-Phuoc, Chuan Li, Lucas Theis +2
We propose a novel generative adversarial network (GAN) for the task of unsupervised learning of 3D representations from natural images. Most generative models rely on 2D kernels t…
RenderNet: A deep convolutional network for differentiable rendering from 3D shapes
Thu Nguyen-Phuoc, Chuan Li, Stephen Balaban +1
Traditional computer graphics rendering pipeline is designed for procedurally generating 2D quality images from 3D shapes with high performance. The non-differentiability due to di…
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
Chuan Li, Michael Wand
This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative neural networks for efficient texture synthesis. While deep neural network a…