RESEARCH
COMPUTER VISION
Live Face De-Identification in Video
October 27, 2019
Abstract
We propose a method for face de-identification that enables fully automatic video modification at high frame rates. The goal is to maximally decorrelate the identity, while having the perception (pose, illumination and expression) fixed. We achieve this by a novel feed-forward encoder-decoder network architecture that is conditioned on the high-level representation of a person’s facial image. The network is global, in the sense that it does not need to be retrained for a given video or for a given identity, and it creates natural looking image sequences with little distortion in time.
AUTHORS
Related Publications
June 15, 2019
COMPUTER VISION
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search | Facebook AI Research
Designing accurate and efficient ConvNets for mobile devices is challenging because the design space is combinatorially large. Due to this, previous neural architecture search (NAS) methods are computationally expensive. ConvNet architecture…
Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer
June 15, 2019
April 28, 2019
COMPUTER VISION
Inverse Path Tracing for Joint Material and Lighting Estimation | Facebook AI Research
Modern computer vision algorithms have brought significant advancement to 3D geometry reconstruction. However, illumination and material reconstruction remain less studied, with current approaches assuming very simplified models for materials…
Dejan Azinović, Tzu-Mao Li, Anton Kaplanyan, Matthias Nießner
April 28, 2019
June 14, 2019
COMPUTER VISION
Thinking Outside the Pool: Active Training Image Creation for Relative Attributes | Facebook AI Research
Current wisdom suggests more labeled image data is always better, and obtaining labels is the bottleneck. Yet curating a pool of sufficiently diverse and informative images is itself a challenge. In particular, training image curation is…
Aron Yu, Kristen Grauman
June 14, 2019
September 09, 2018
COMPUTER VISION
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs | Facebook AI Research
Consumer depth sensors are more and more popular and come to our daily lives marked by its recent integration in the latest iPhone X. However, they still suffer from heavy noises which dramatically limit their applications. Although plenty of…
Shi Yan, Chenglei Wu, Lizhen Wang, Feng Xu, Liang An, Kaiwen Guo, Yebin Liu
September 09, 2018
