Summary of Enhancing Bandwidth Efficiency For Video Motion Transfer Applications Using Deep Learning Based Keypoint Prediction, by Xue Bai et al.
Enhancing Bandwidth Efficiency for Video Motion Transfer Applications using Deep Learning Based Keypoint Prediction
by Xue Bai, Tasmiah Haque, Sumit Mohan, Yuliang Cai, Byungheon Jeong, Adam Halasz, Srinjoy Das
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a deep learning-based framework for reducing bandwidth in video applications such as video conferencing and virtual reality gaming. The framework uses the First Order Motion Model (FOMM) to represent dynamic objects, which are extracted by a self-supervised keypoint detector and organized into a time series. A Variational Recurrent Neural Network (VRNN) is used to predict keypoints, enabling transmission at lower frames per second on the source device. The predicted keypoints are then synthesized into video frames using an optical flow estimator and generator network. The paper demonstrates the effectiveness of this approach on three diverse datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to make videos use less bandwidth without losing quality. It uses a special model called FOMM to track moving objects in videos, which helps predict what will happen next. This allows for more efficient transmission and compression. The approach is tested on different types of videos and shows promising results. |
Keywords
» Artificial intelligence » Deep learning » Neural network » Optical flow » Self supervised » Time series
