MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones
Ang Li, Chunpeng Wu, Yiran Chen, Bin Ni

TL;DR
MVStylizer is a novel edge-assisted system that enables efficient, high-quality photorealistic video style transfer on mobile phones by combining edge computing, optical-flow-based interpolation, and federated learning.
Contribution
It introduces a new system integrating edge computing, frame interpolation, and federated learning to perform real-time video style transfer on mobile devices, overcoming computational limitations.
Findings
Achieves 75.5× speedup on 1080p videos.
Generates stylized videos with better visual quality than state-of-the-art.
Effectively leverages federated learning for continuous model improvement.
Abstract
Recent research has made great progress in realizing neural style transfer of images, which denotes transforming an image to a desired style. Many users start to use their mobile phones to record their daily life, and then edit and share the captured images and videos with other users. However, directly applying existing style transfer approaches on videos, i.e., transferring the style of a video frame by frame, requires an extremely large amount of computation resources. It is still technically unaffordable to perform style transfer of videos on mobile phones. To address this challenge, we propose MVStylizer, an efficient edge-assisted photorealistic video style transfer system for mobile phones. Instead of performing stylization frame by frame, only key frames in the original video are processed by a pre-trained deep neural network (DNN) on edge servers, while the rest of stylized…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
