Unsupervised Video Prediction from a Single Frame by Estimating 3D Dynamic Scene Structure. (arXiv:2106.09051v1 [cs.CV])

Our goal in this work is to generate realistic videos given just one initial
frame as input. Existing unsupervised approaches to this task do not consider
the fact that a video typically shows a 3D environment, and that this should
remain coherent from frame to frame even as the camera and objects move. We
address this by developing a model that first estimates the latent 3D structure
of the scene, including the segmentation of any moving objects. It then
predicts future frames by simulating the object and camera dynamics, and
rendering the resulting views. Importantly, it is trained end-to-end using only
the unsupervised objective of predicting future frames, without any 3D
information nor segmentation annotations. Experiments on two challenging
datasets of natural videos show that our model can estimate 3D structure and
motion segmentation from a single frame, and hence generate plausible and
varied predictions.

Source: https://arxiv.org/abs/2106.09051


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