Inserting Videos into Videos
Donghoon Lee, Tomas Pfister, Ming-Hsuan Yang

TL;DR
This paper presents a novel method for inserting videos into other videos realistically, using a neural network trained with both real and synthetic data, capable of handling complex scenes and motions.
Contribution
Introduces a new video insertion problem and a neural network architecture that combines supervised and unsupervised learning with synthetic data for realistic video synthesis.
Findings
Successfully inserts videos into complex scenes
Synthesizes realistic long video sequences
Handles various object motions and backgrounds
Abstract
In this paper, we introduce a new problem of manipulating a given video by inserting other videos into it. Our main task is, given an object video and a scene video, to insert the object video at a user-specified location in the scene video so that the resulting video looks realistic. We aim to handle different object motions and complex backgrounds without expensive segmentation annotations. As it is difficult to collect training pairs for this problem, we synthesize fake training pairs that can provide helpful supervisory signals when training a neural network with unpaired real data. The proposed network architecture can take both real and fake pairs as input and perform both supervised and unsupervised training in an adversarial learning scheme. To synthesize a realistic video, the network renders each frame based on the current input and previous frames. Within this framework, we…
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Taxonomy
TopicsDigital Media Forensic Detection · Generative Adversarial Networks and Image Synthesis · Advanced Image Processing Techniques
