  In this paper, we introduce a stochastic dynamics video infilling (SDVI)
framework to generate frames between long intervals in a video. Our task
differs from video interpolation which aims to produce transitional frames for
a short interval between every two frames and increase the temporal resolution.
Our task, namely video infilling, however, aims to infill long intervals with
plausible frame sequences. Our framework models the infilling as a constrained
stochastic generation process and sequentially samples dynamics from the
inferred distribution. SDVI consists of two parts: (1) a bi-directional
constraint propagation module to guarantee the spatial-temporal coherence among
frames, (2) a stochastic sampling process to generate dynamics from the
inferred distributions. Experimental results show that SDVI can generate clear
frame sequences with varying contents. Moreover, motions in the generated
sequence are realistic and able to transfer smoothly from the given start frame
to the terminal frame. Our project site is
https://xharlie.github.io/projects/project_sites/SDVI/video_results.html
