Stochastic Video Long-term Interpolation
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Video interpolation is aiming to generate intermediate sequence between two frames. While most existing studies require the two reference frames to be consecutive, we propose a stochastic learning frame work that can infer a possible intermediate sequence between a long interval. Therefore, our work expands the usability of video interpolation in applications such as video long-term temporal super-resolution, missing frames repair and motion dynamic inference. Our model includes a deterministic estimation to guarantee the spatial and temporal coherency among the generated frames and a stochastic mechanism to sample sequences from possible realities. Like the studies of stochastic video prediction, our generated sequences are both sharp and varied. In addition, most of the motions are realistic and can smoothly transition from the referred start frame to the end frame.
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