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TGS2024
research.nvidia.com
1 LMU Munich, 2 NVIDIA, 3 Vector Institute, 4 University of Toronto, 5 University of Waterloo Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. We first pr
We generate a 3D SDF and a texture field via two latent codes. We utilize DMTet to extract a 3D surface mesh from the SDF, and query the texture field at surface points to get colors. We train with adversarial losses defined on 2D images. In particular, we use a rasterization-based differentiable renderer to obtain RGB images and silhouettes. We utilize two 2D discriminators, each on RGB image, an
Current state-of-the-art methods for image segmentation form a dense image representation where the color, shape and texture information are all processed together inside a deep CNN. This however may not be ideal as they contain very different type of information relevant for recognition. We propose a new architecture that adds a shape stream to the classical CNN architecture. The two streams proc
Publications Progressive Growing of GANs for Improved Quality, Stability, and Variation Progressive Growing of GANs for Improved Quality, Stability, and Variation Picture: Two imaginary celebrities that were dreamed up by a random number generator. Abstract: We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator prog
Publications Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the var
Publications Spatiotemporal Variance-Guided Filtering: Real-Time Reconstruction for Path-Traced Global Illumination Spatiotemporal Variance-Guided Filtering: Real-Time Reconstruction for Path-Traced Global Illumination We introduce a reconstruction algorithm that generates a temporally stable sequence of images from one path-per-pixel global illumination. To handle such noisy input, we use tempora
Publications MCM-GPU: Multi-Chip-Module GPUs for Continued Performance Scalability Historically, improvements in GPU-based high performance computing have been tightly coupled to transistor scaling. As Moore's law slows down, and the number of transistors per die no longer grows at historical rates, the performance curve of single monolithic GPUs will ultimately plateau. However, the need for high
Publications An Adaptive Acceleration Structure for Screen-space Ray Tracing We propose an efficient acceleration structure for real-time screen-space ray tracing. The hybrid data structure represents the scene geometry by combining a bounding volume hierarchy with local planar approximations. This enables fast empty space skipping while tracing and yields exact intersection points for the planar
Functional languages provide a solid foundation on which complex optimization passes can be designed to exploit available parallelism in the underlying system. Their mathematical foundations enable high-level optimizations that would be impossible in traditional imperative languages. This makes them uniquely suited for generation of efficient target code for parallel systems, such as multiple Cent
Subpixel Reconstruction Antialiasing (SRAA) combines single-pixel (1x) shading with subpixel visibility to create antialiased images without increasing the shading cost. SRAA targets deferred-shading renderers, which cannot use multisample antialiasing. SRAA operates as a post-process on a rendered image with superresolution depth and normal buffers, so it can be incorporated into an existing rend
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