  Temporal consistency is crucial for extending image processing pipelines to
the video domain, which is often enforced with flow-based warping error over
adjacent frames. Yet for human video synthesis, such scheme is less reliable
due to the misalignment between source and target video as well as the
difficulty in accurate flow estimation. In this paper, we propose an effective
intrinsic temporal regularization scheme to mitigate these issues, where an
intrinsic confidence map is estimated via the frame generator to regulate
motion estimation via temporal loss modulation. This creates a shortcut for
back-propagating temporal loss gradients directly to the front-end motion
estimator, thus improving training stability and temporal coherence in output
videos. We apply our intrinsic temporal regulation to single-image generator,
leading to a powerful "INTERnet" capable of generating $512\times512$
resolution human action videos with temporal-coherent, realistic visual
details. Extensive experiments demonstrate the superiority of proposed INTERnet
over several competitive baselines.
