  Despite the advances in the field of generative models in computer vision,
video stabilization still lacks a pure regressive deep-learning-based
formulation. Deep video stabilization is generally formulated with the help of
explicit motion estimation modules due to the lack of a dataset containing
pairs of videos with similar perspective but different motion. Therefore, the
deep learning approaches for this task have difficulties in the pixel-level
synthesis of latent stabilized frames, and resort to motion estimation modules
for indirect transformations of the unstable frames to stabilized frames,
leading to the loss of visual content near the frame boundaries. In this work,
we aim to declutter this over-complicated formulation of video stabilization
with the help of a novel dataset that contains pairs of training videos with
similar perspective but different motion, and verify its effectiveness by
successfully learning motion blind full-frame video stabilization through
employing strictly conventional generative techniques and further improve the
stability through a curriculum-learning inspired adversarial training strategy.
Through extensive experimentation, we show the quantitative and qualitative
advantages of the proposed approach to the state-of-the-art video stabilization
approaches. Moreover, our method achieves $\sim3\times$ speed-up over the
currently available fastest video stabilization methods.
