Learning Self-Supervised Space-Time CNN for Fast Video Style Transfer

Style transfer on images has achieved significant advances in recent years, with the deep convolutional neural network (CNN). Directly applying image style transfer algorithms to each frame of a video independently often leads to flickering and unstable results. In this work, we present a self-supervised space-time convolutional neural network (CNN) based method for online video style transfer, named as VTNet, which is end-to-end trained from nearly unlimited unlabeled video data to produce temporally coherent stylized videos in real-time. Specifically, our VTNet transfer the style of a reference image to the source video frames, which is formed by the temporal prediction branch and the stylizing branch. The temporal prediction branch is used to capture discriminative spatiotemporal features for temporal consistency, pretrained in an adversarial manner from unlabeled video data. The stylizing branch is used to transfer the style image to a video frame with the guidance from the temporal prediction branch to ensure temporal consistency. To guide the training of VTNet, we introduce the style-coherence loss net (SCNet), which assembles the content loss, the style loss, and the new designed coherence loss. These losses are computed based on high-level features extracted from a pretrained VGG-16 network. The content loss is used to preserve high-level abstract contents of the input frames, and the style loss introduces new colors and patterns from the style image. Instead of using optical flow to explicitly redress the stylized video frames, we design the coherence loss to make the stylized video inherit the dynamics and motion patterns from the source video to remove temporal flickering. Extensive subjective and objective evaluations on various styles demonstrate that the proposed method achieves favorable results against the state-of-the-arts with high efficiency.