Wish You Were Here: Context-Aware Human Generation

June 13, 2020


We present a novel method for inserting objects, specifically humans, into existing images, such that they blend in a photorealistic manner, while respecting the semantic context of the scene. Our method involves three subnetworks: the first generates the semantic map of the new person, given the pose of the other persons in the scene and an optional bounding box specification. The second network renders the pixels of the novel person and its blending mask, based on specifications in the form of multiple appearance components. A third network refines the generated face in order to match those of the target person. Our experiments present convincing high-resolution outputs in this novel and challenging application domain. In addition, the three networks are evaluated individually, demonstrating for example, state of the art results in pose transfer benchmarks.

Download the Paper


Written by

Oran Gafni

Lior Wolf


Conference on Computer Vision and Pattern Recognition (CVPR)

Recent Publications

January 01, 2021

Asynchronous Gradient-Push | Facebook AI Research

We consider a multi-agent framework for distributed optimization where each agent has access to a local smooth strongly convex function, and the collective goal is to achieve consensus on the parameters that minimize the sum of the agents’…

Mahmoud Assran, Michael Rabbat

January 01, 2021

August 22, 2020

GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce

In this paper we propose image classification modeling technique targeted for marketplace. We use public posts from marketplace and search log interactions for training image classifier and achieve significant improvements in e-commerce in comparison to previous version of our image classifier.

Sean Bell, Yiqun Liu, Sami Alsheikh, Yina Tang, Ed Pizzi, M. Henning, Karun Singh, Omkar Parkhi, Fedor Borisyuk

August 22, 2020

June 16, 2020


PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

Due to memory limitations in current hardware, previous approaches tend to take low resolution images as input to cover large spatial context, and produce less precise (or low resolution) 3D estimates as a result. We address this limitation by formulating a multi-level architecture that is end-to-end trainable

Shunsuke Saito, Tomas Simon, Jason Saragih, Hanbyul Joo

June 16, 2020

June 14, 2020

Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA | Facebook AI Research

Many visual scenes contain text that carries crucial information, and it is thus essential to understand text in images for downstream reasoning tasks. For example, a deep water label on a warning sign warns people about the danger in the…

Ronghang Hu, Amanpreet Singh, Trevor Darrell, Marcus Rohrbach

June 14, 2020


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.