NVIDIA Toronto Lab

NVIDIA Toronto Lab is the research portal for the Toronto-based AI research division of NVIDIA Corporation, publishing open-source implementations of their papers on neural rendering, generative modeling, and 3D scene reconstruction. The portal hosts project pages for publications appearing at SIGGRAPH, CVPR, and NeurIPS, each accompanied by downloadable source code repositories, pretrained model weights, demonstration videos, and supplementary materials. Notable projects include GET3D for generating high-quality 3D textured shapes from natural categories, GauGAN and its successor for converting semantic segmentation maps into photorealistic landscapes, the style-based video generation pipeline that transforms source footage into different visual domains, and the large-scale synthetic data engine producing annotated driving scenes for autonomous vehicle training. The code releases typically include training scripts with hyperparameter configurations, inference notebooks for reproducing results on custom inputs, and data preprocessing pipelines. The pretrained models are distributed as checkpoint files loadable in PyTorch, with documented architecture specifications and layer configurations. The demonstration videos show qualitative results comparing the proposed method against baselines on standard benchmarks, with side-by-side comparisons and failure case analysis. The datasets released include synthetic scene collections with ground truth annotations for depth, segmentation, and optical flow. The GitHub repositories under the nv-tlabs organization contain the full implementation stack from data loaders through training loops to evaluation metrics.