Research

DeepFocus: Learned Image Synthesis for Computational Displays

December 4, 2018

Abstract

https://research.fb.com/wp-content/uploads/2018/11/DeepFocus_SIGGRAPH_Asia.mp4

Addressing vergence-accommodation conflict in head-mounted displays (HMDs) requires resolving two interrelated problems. First, the hardware must support viewing sharp imagery over the full accommodation range of the user. Second, HMDs should accurately reproduce retinal defocus blur to correctly drive accommodation. A multitude of accommodation-supporting HMDs have been proposed, with three architectures receiving particular attention: varifocal, multifocal, and light field displays. These designs all extend depth of focus, but rely on computationally expensive rendering and optimization algorithms to reproduce accurate defocus blur (often limiting content complexity and interactive applications). To date, no unified framework has been proposed to support driving these emerging HMDs using commodity content. In this paper, we introduce DeepFocus, a generic, end-to-end convolutional neural network designed to efficiently solve the full range of computational tasks for accommodation-supporting HMDs. This network is demonstrated to accurately synthesize defocus blur, focal stacks, multilayer decompositions, and multiview imagery using only commonly available RGB-D images, enabling real-time, near-correct depictions of retinal blur with a broad set of accommodation-supporting HMDs.

Download Supplementary Materials

Download the Paper

Related Publications

June 05, 2026

Conversational AI

Ranking & Recommendations

Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval

Anshumali Shrivastava, Jason Chen, Qi Ma, Zeyu Yang

June 05, 2026

May 26, 2026

Human & Machine Intelligence

Theory

Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images

Valentin Wyart, Huy V. Vo, Jean Remi King, Josephine Raugel, Jérémy Rapin, Marc Szafraniec, Max Seitzer, Patrick Labatut, Piotr Bojanowski

May 26, 2026

May 19, 2026

Human & Machine Intelligence

EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data

Alvin W. M. Tan, Nicolas Hamilakis, Manel Khentout, Sho Tsuji, Balázs Kégl, Michael C. Frank, Angel Villar Corrales, Charles-Eric Saint-James, Dongyan Lin, Emmanuel Dupoux, Jiayi Shen, Juan Pino, Mahi Luthra, Martin Gleize, Phillip Rust, Rashel Moritz, Sheila Krogh-Jespersen, Surya Parimi, Tom Fizycki, Vanessa Stark, Yosuke Higuchi, Youssef Benchekroun

May 19, 2026

May 17, 2026

Conversational AI

GIM: Evaluating models via tasks that integrate multiple cognitive domains

Alexandre Rezende, Rohit Patel, Steven McClain

May 17, 2026

October 31, 2019

NLP

Facebook AI's WAT19 Myanmar-English Translation Task Submission

Peng-Jen Chen, Jiajun Shen, Matt Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc’Aurelio Ranzato

October 31, 2019

October 27, 2019

Order-Aware Generative Modeling Using the 3D-Craft Dataset | Facebook AI Research

Zhuoyuan Chen, Demi Guo, Tong Xiao, Saining Xie, Xinlei Chen, Haonan Yu, Jonathan Gray, Kavya Srinet, Haoqi Fan, Jerry Ma, Charles R. Qi, Shubham Tulsiani, Arthur Szlam, Larry Zitnick

October 27, 2019

April 25, 2020

Energy-Based Models for Atomic-Resolution Protein Conformations | Facebook AI Research

Yilun Du, Joshua Meier, Jerry Ma, Rob Fergus, Alexander Rives

April 25, 2020

June 11, 2019

Computer Vision

ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero | Facebook AI Research

Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick

June 11, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.