977 citations · 1.2k across the 10 of their papers we have counts for
10 papers
Training-Free Neural Matte Extraction for Visual Effects
Sharif Elcott, J. P. Lewis, Nori Kanazawa +1
Alpha matting is widely used in video conferencing as well as in movies, television, and social media sites. Deep learning approaches to the matte extraction problem are well suite…
ACM Multimedia Grand Challenge on Detecting Cheapfakes
Shivangi Aneja, Cise Midoglu, Duc-Tien Dang-Nguyen +5
Cheapfake is a recently coined term that encompasses non-AI (``cheap'') manipulations of multimedia content. Cheapfakes are known to be more prevalent than deepfakes. Cheapfake med…
MMSys'21 Grand Challenge on Detecting Cheapfakes
Shivangi Aneja, Cise Midoglu, Duc-Tien Dang-Nguyen +5
Cheapfake is a recently coined term that encompasses non-AI ("cheap") manipulations of multimedia content. Cheapfakes are known to be more prevalent than deepfakes. Cheapfake media…
COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning
Shivangi Aneja, Chris Bregler, Matthias Nießner
Despite the recent attention to DeepFakes, one of the most prevalent ways to mislead audiences on social media is the use of unaltered images in a new but false context. To address…
SimPose: Effectively Learning DensePose and Surface Normals of People from Simulated Data
Tyler Zhu, Per Karlsson, Christoph Bregler
With a proliferation of generic domain-adaptation approaches, we report a simple yet effective technique for learning difficult per-pixel 2.5D and 3D regression representations of…
Cross-Domain Image Manipulation by Demonstration
Ben Usman, Nick Dufour, Kate Saenko +1
In this work we propose a model that can manipulate individual visual attributes of objects in a real scene using examples of how respective attribute manipulations affect the outp…
MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
Arjun Jain, Jonathan Tompson, Yann LeCun +1
In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and…
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
Jonathan Tompson, Arjun Jain, Yann LeCun +1
This paper proposes a new hybrid architecture that consists of a deep Convolutional Network and a Markov Random Field. We show how this architecture is successfully applied to the…
Learning Human Pose Estimation Features with Convolutional Networks
Arjun Jain, Jonathan Tompson, Mykhaylo Andriluka +2
This paper introduces a new architecture for human pose estimation using a multi- layer convolutional network architecture and a modified learning technique that learns low-level f…
Markerless Motion Capture in the Crowd
Ian Spiro, Thomas Huston, Christoph Bregler
This work uses crowdsourcing to obtain motion capture data from video recordings. The data is obtained by information workers who click repeatedly to indicate body configurations i…