Context-aware Synthesis for Video Frame Interpolation (1803.10967v1)

Abstract: Video frame interpolation algorithms typically estimate optical flow or its variations and then use it to guide the synthesis of an intermediate frame between two consecutive original frames. To handle challenges like occlusion, bidirectional flow between the two input frames is often estimated and used to warp and blend the input frames. However, how to effectively blend the two warped frames still remains a challenging problem. This paper presents a context-aware synthesis approach that warps not only the input frames but also their pixel-wise contextual information and uses them to interpolate a high-quality intermediate frame. Specifically, we first use a pre-trained neural network to extract per-pixel contextual information for input frames. We then employ a state-of-the-art optical flow algorithm to estimate bidirectional flow between them and pre-warp both input frames and their context maps. Finally, unlike common approaches that blend the pre-warped frames, our method feeds them and their context maps to a video frame synthesis neural network to produce the interpolated frame in a context-aware fashion. Our neural network is fully convolutional and is trained end to end. Our experiments show that our method can handle challenging scenarios such as occlusion and large motion and outperforms representative state-of-the-art approaches.

• The paper presents a context-aware synthesis method that integrates pixel-level contextual maps with advanced optical flow to enhance video frame interpolation.
• It employs a tailored GridNet-based synthesis network for multi-scale processing, improving interpolation quality under complex motion and occlusions.
• Experimental results show state-of-the-art performance on benchmarks like Middlebury, suggesting significant potential for video editing and augmented reality applications.

Context-aware Synthesis for Video Frame Interpolation

This paper addresses the inherent challenges in video frame interpolation by introducing a context-aware synthesis approach aimed at generating high-quality intermediate frames between any two consecutive original video frames. Traditional interpolation methods predominantly rely on optical flow or its variants to estimate and guide frame synthesis, facing hurdles in achieving accurate results, especially under conditions of large motion and occlusion. The proposed method significantly diverges from existing methodologies by leveraging pixel-wise contextual information alongside motion vectors to enhance interpolation precision.

The authors leverage advances in pre-trained convolutional neural networks (CNNs) and robust optical flow algorithms to develop their method. Specifically, the approach employs a neural network to extract extensive contextual information from input frames—these context maps encode pixel neighborhood characteristics beyond motion, thus enriching the synthesis process with detailed imagery cues. Subsequently, the incorporation of state-of-the-art bidirectional optical flow algorithms, such as the PWC-Net, facilitates a key step of the framework in warping both the input frames and the context maps. This combination enables the algorithm to capture and adapt to complex motion patterns between frames, which is often the Achilles heel of traditional optical flow-based interpolation techniques.

A pivotal component of the framework is a bespoke frame synthesis network, which integrates a novel extension of the GridNet architecture. This structure allows for multi-scale processing, thus facilitating the blending of detailed local and holistic context information—which is imperative in dynamically addressing occlusions and flow inaccuracies. The synthesis network distinguishes itself from comparable architectures by bypassing the limitations of pixel-wise blending; instead, it offers a more flexible synthesis mechanism that draws on neighborhood pixels to enhance interpolation quality. This is particularly relevant in challenging scenarios where traditional pixel blending would falter.

The implementation details underscore the level of sophistication involved in practical deployment. By leveraging CUDA and cuDNN for computational efficiency and training the network using a well-curated dataset of video patches, the authors ensure a robust and scalable deployment of the model. The method benchmarks significantly well, with the paper reporting the highest score against public datasets like the Middlebury evaluation set, indicating a notable stride in video interpolation capabilities.

In terms of contribution, this research has promising implications for both the theoretical understanding and practical applications of video frame interpolation. From a theoretical standpoint, the successful integration of contextual information with optical flow presents a compelling case for future exploration into contextually aware algorithms across other domains of computer vision. Practically, the paper's results hint at transformative applications in video editing, frame rate conversion, and augmented reality, where seamless and realistic interpolation is paramount.

Further development along the lines of integrating adversarial training could enhance perceptual quality, similar to successes seen in image synthesis. Improved training datasets could offer diverse motion and texture scenarios, potentially refining model robustness. Lastly, ongoing improvements in optical flow algorithms will naturally complement and potentiate the method presented, indicating a fertile ground for continued exploration and innovation in this domain.