Real-time video frame interpolation (VFI) is very useful in video processing, media players, and display devices. We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for VFI. To realize a high-quality flow-based VFI method, RIFE uses a neural network named IFNet that can estimate the intermediate flows end-to-end with much faster speed. A privileged distillation scheme is designed for stable IFNet training and improve the overall performance. RIFE does not rely on pre-trained optical flow models and can support arbitrary-timestep frame interpolation with the temporal encoding input. Experiments demonstrate that RIFE achieves state-of-the-art performance on several public benchmarks. Compared with the popular SuperSlomo and DAIN methods, RIFE is 4--27 times faster and produces better results. Furthermore, RIFE can be extended to wider applications thanks to temporal encoding. The code is available at https://github.com/megvii-research/ECCV2022-RIFE.

Excellent painters can use only a few strokes to create a fantastic painting, which is a symbol of human intelligence and art. Reversing the simulator to interpret images is also a challenging task of computer vision in recent years. In this paper, we present SARA, a stroke-based artistic rendering agent that combines the neural renderer and deep reinforcement learning (DRL), allowing the machine to learn the ability to deconstruct images using strokes and create amazing visual effects. Our agent is an end-to-end program that converts natural images into paintings. The training process does not require the experience of human painting or stroke tracking data.

The stroke sequence of characters is significant for the character recognition task. In this paper, we propose a stroke-based character recognition (SCR) method. We train a stroke inference module under deep reinforcement learning (DRL) framework. This module extracts the sequence of strokes from characters, which can be integrated with character recognizers to improve their robustness to noise. Our experiments show that the module can handle complicated noise and reconstruct the characters. Meanwhile, it can also help achieve great ability in defending adversarial attacks of character recognizers.