Depth-Aware Video Frame Interpolation
This work improves video frame interpolation for applications like video editing and playback by handling occlusion more effectively, though it is incremental as it builds on existing deep learning approaches.
The paper tackles video frame interpolation by addressing quality reduction from large object motion or occlusion, proposing a depth-aware method that uses depth information to detect occlusion and synthesize intermediate flows, resulting in favorable performance against state-of-the-art methods on various datasets.
Video frame interpolation aims to synthesize nonexistent frames in-between the original frames. While significant advances have been made from the recent deep convolutional neural networks, the quality of interpolation is often reduced due to large object motion or occlusion. In this work, we propose a video frame interpolation method which explicitly detects the occlusion by exploring the depth information. Specifically, we develop a depth-aware flow projection layer to synthesize intermediate flows that preferably sample closer objects than farther ones. In addition, we learn hierarchical features to gather contextual information from neighboring pixels. The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame. Our model is compact, efficient, and fully differentiable. Quantitative and qualitative results demonstrate that the proposed model performs favorably against state-of-the-art frame interpolation methods on a wide variety of datasets.