Samuli Laine
Member of NVIDIA Research since 2007; first author of nvdiffrast and co-author of StyleGAN and EDM
- Member of NVIDIA Research since 2007, NVIDIA Research1
Samuli Laine joined NVIDIA Research in 2007, and NVIDIA's page says Laine's research has ranged from photorealistic and real-time rendering to voxel-based graphics, GPU ray tracing, and 3D content creation, with a current focus on deep learning and its applications in computer graphics. Laine is first author of "Modular Primitives for High-Performance Differentiable Rendering", the paper behind NVIDIA's nvdiffrast PyTorch library. Laine is also a co-author of the StyleGAN, StyleGAN3, EDM, and EDM2 generative model papers, whose official implementations NVIDIA publishes in its NVlabs GitHub organization.12345678
Work
nvdiffrast (external site: github.com)Published research
First author of "Modular Primitives for High-Performance Differentiable Rendering" (2020). The nvdiffrast repository, a PyTorch library of primitive operations for rasterization-based differentiable rendering, lists Laine first and is made available under the NVIDIA Source Code License.23
StyleGAN and EDM generative models (external site: github.com)Published research
Second author of the 2018 StyleGAN paper and co-author of StyleGAN3 (2021), EDM (2022), and EDM2 (2023) with Tero Karras, Miika Aittala, Timo Aila, and others. The EDM2 repository releases code and pretrained models under CC BY-NC-SA 4.0.45678
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