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In this paper, we introduce a new problem of manipulating a given video byinserting other videos into it. Our main task is, given an object video and ascene video, to insert the object video at a user-specified location in thescene video so that the resulting video looks realistic. We aim to handledifferent object motions and complex backgrounds without expensive segmentationannotations. As it is difficult to collect training pairs for this problem, wesynthesize fake training pairs that can provide helpful supervisory signalswhen training a neural network with unpaired real data. The proposed networkarchitecture can take both real and fake pairs as input and perform bothsupervised and unsupervised training in an adversarial learning scheme. Tosynthesize a realistic video, the network renders each frame based on thecurrent input and previous frames. Within this framework, we observe thatinjecting noise into previous frames while generating the current framestabilizes training. We conduct experiments on real-world videos in objecttracking and person re-identification benchmark datasets. Experimental resultsdemonstrate that the proposed algorithm is able to synthesize long sequences ofrealistic videos with a given object video inserted.

Inserting Videos into Videos