Research

We present a method for reproducing complex multi-character interactions for physically simulated humanoid characters using deep reinforcement learning.

We present a system that animates children’s drawings of the human figure, is robust to the variance inherent in these depictions, and is simple enough for anyone to use.

Focus on the underexplored question of how to personalize these systems while preserving privacy.

Meta deploys large-scale distributed storage services across datacenters. Storage applications are often categorized based on the type and temperature of the data stored: hot, ...

Proposing Point Straight Flow, a model that exhibits impressive performance using one step.

Propose a framework based on diffusion models for consistent and realistic long-term novel view synthesis. Diffusion models have achieved impressive performance on many content creation applications, such as image-to-image translation and text-to- image generation.

we introduce an alternative formulation called “user-centric ranking” based on a transposed view, which casts ‘users’ as ‘tokens’ and ‘items’ as ‘documents’ instead. We show that this formulation has a number of advantages and shows less sign of quality saturation when trained on substantially larger data sets.

Recognizing human activities is a decades-old problem in computer vision. With recent advancements in user- assistive augmented reality and virtual reality (AR/VR) systems...

We propose a model, called Cube R-CNN, designed to generalize across camera and scene types with a unified approach. We show that Cube R-CNN outperforms prior works on the larger...

We present the design of a productionized end-to-end stereo depth sensing system that does pre-processing, online stereo rectification, and stereo depth estimation with...