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Mask3D: Mask Transformer for 3D Instance Segmentation

Modern 3D semantic instance segmentation approaches predominantly rely on specialized voting mechanisms followed by carefully designed geometric clustering techniques. Building on the successes of recent Transformer-based methods for object detection …

Magic3D: High-Resolution Text-to-3D Content Creation

DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results. However, the method has two inherent limitations: (a) …

Omniverse AI Toybox

NVIDIA is using AI to improve the Content Creation process, giving the power to create virtual worlds to creators everywhere. AI Toybox is a collection of Omniverse extensions showcasing state of the art NVIDIA research aimed at empowering content …

Neural Brushstroke Engine: Learning a Latent Style Space of Interactive Drawing Tools

Neural Brushstroke Engine (NeuBE) includes a GAN model that learns to mimic many drawing media styles by learning from unlabeled images. The result is a GAN model that can be directly controlled by user strokes, with style code z corresponding to the …

GENIE: Higher-Order Denoising Diffusion Solvers

Denoising diffusion models (DDMs) have emerged as a powerful class of generative models. A forward diffusion process slowly perturbs the data, while a deep model learns to gradually denoise. Synthesis amounts to solving a differential equation (DE) …

GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images

As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train …

LION: Latent Point Diffusion Models for 3D Shape Generation

Denoising diffusion models (DDMs) have shown promising results in 3D point cloud synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such …

Optimizing data collection for machine learning

Modern deep learning systems require huge data sets to achieve impressive performance, but there is little guidance on how much or what kind of data to collect. Over-collecting data incurs unnecessary present costs, while under-collecting may incur …

XDGAN: Multi-Modal 3D Shape Generation in 2D Space

Generative models for 2D images has recently seen tremendous progress in quality, resolution and speed as a result of the efficiency of 2D convolutional architectures. However it is difficult to extend this progress into the 3D domain since most …

Kaolin Wisp: A PyTorch Library and Engine for Neural Fields Research

NVIDIA Kaolin Wisp is a PyTorch library powered by [NVIDIA Kaolin Core](https://nv-tlabs.github.io/publication/kaolin/) to work with neural fields (including NeRFs, NGLOD, instant-ngp and VQAD). NVIDIA Kaolin Wisp aims to provide a set of common …