Computer Vision

UniCon: Universal Neural Controller For Physics-based Character Motion

The field of physics-based animation is gaining importance due to the increasing demand for realism in video games and films, and has recently seen wide adoption of data-driven techniques, such as deep reinforcement learning (RL), which learn control …

Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research

Kaolin is a PyTorch library aiming to accelerate 3D deep learning research. Kaolin provides efficient implementations of differentiable 3D modules for use in deep learning systems. With functionality to load and preprocess several popular 3D …

Variational Amodal Object Completion

Learning Deformable Tetrahedral Meshes for 3D Reconstruction

3D shape representations that accommodate learning-based 3D reconstruction are an open problem in machine learning and computer graphics. Previous work on neural 3D reconstruction demonstrated benefits, but also limitations, of point cloud, voxel, …

Meta-Sim2: Unsupervised Learning of Scene Structure for Synthetic Data Generation

Procedural models are being widely used to synthesize scenes for graphics, gaming, and to create (labeled) synthetic datasets for ML. In order to produce realistic and diverse scenes, a number of parameters governing the procedural models have to be …

Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D

The goal of perception for autonomous vehicles is to extract semantic representations from multiple sensors and fuse these representations into a single 'bird's-eye-view' coordinate frame for consumption by motion planning. We propose a new …