Research

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.

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

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, ...

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.

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

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.

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...

In this work, we present a fully binarized distance computing (BinDC) framework to perform distance computations for few-shot learning using only accumulation and logic operations.

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...