Video frame synthesis is an active computer vision problem which has applications in video compression, streaming, editing, and understanding. In this work, we present a computational high speed video synthesis framework. Our framework takes as inputs two types of data streams: an intensity frame stream and a neuromorphic event stream, which consists of asynchronous bipolar "events" which encode brightness variations over time at over 1000 fps. We introduce an algorithm to recover a space-time video from these two modes of observations. We factor the reconstruction into a physical model-based reconstruction (PBR) process and a residual denoising process. We use a differentiable model to approximate the physical sensing process, which enables stochastic gradient descent optimization using automatic differentiation. Residual errors in PBR reconstruction are further reduced by training a residual denoiser to remove reconstruction artifacts. The video output from our reconstruction algorithm has both high frame rate and well-recovered spatial features. Our framework is capable of handling challenging scenes that include fast motion and strong occlusions.
Video frame synthesis. Video frame synthesis is a classic computer vision topic and has applications in video compression, frame rate conversion, motion field estimation, and video editing rendering. Early work on video frame interpolation and extrapolation focused on establishing block-wise @cite_29 @cite_38 and or pixel-wise @cite_22 @cite_7 correspondences between available frames. In particular, pixel-wise optical flow methods are commonly used, which assume local brightness constancy @cite_15 @cite_31 . Implementation schemes for improved performances include coarse-to-fine estimation @cite_11 , texture decomposition @cite_45 , and deep networks @cite_8 . Recent work has focused on constructing end-to-end deep neural networks to bypass optical flow estimation. In @cite_17 , a voxel flow layer was inserted in a convolutional neural network (CNN). Niklaus al @cite_13 forms frame interpolation as a local convolution process and trained a CNN with gradient loss and color loss. Novel architectures, , Generative Adversarial Networks (GAN) @cite_21 , and loss functions, , transitive consistency loss @cite_18 , have also been proposed for multi-scale frame interpolation and extrapolation.
@cite_38 @cite_31 @cite_18 @cite_22 @cite_7 @cite_8 @cite_29 @cite_21 @cite_17 @cite_45 @cite_15 @cite_13 @cite_11
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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. Video frame interpolation typically involves two steps: motion estimation and pixel synthesis. Such a two-step approach heavily depends on the quality of motion estimation. This paper presents a robust video frame interpolation method that combines these two steps into a single process. Specifically, our method considers pixel synthesis for the interpolated frame as local convolution over two input frames. The convolution kernel captures both the local motion between the input frames and the coefficients for pixel synthesis. Our method employs a deep fully convolutional neural network to estimate a spatially-adaptive convolution kernel for each pixel. This deep neural network can be directly trained end to end using widely available video data without any difficult-to-obtain ground-truth data like optical flow. Our experiments show that the formulation of video interpolation as a single convolution process allows our method to gracefully handle challenges like occlusion, blur, and abrupt brightness change and enables high-quality video frame interpolation. Recently, convolutional neural networks (CNN) have been successfully applied to view synthesis problems. However, such CNN-based methods can suffer from lack of texture details, shape distortions, or high computational complexity. In this paper, we propose a novel CNN architecture for view synthesis called Deep View Morphing that does not suffer from these issues. To synthesize a middle view of two input images, a rectification network first rectifies the two input images. An encoder-decoder network then generates dense correspondences between the rectified images and blending masks to predict the visibility of pixels of the rectified images in the middle view. A view morphing network finally synthesizes the middle view using the dense correspondences and blending masks. We experimentally show the proposed method significantly outperforms the state-of-the-art CNN-based view synthesis method. Standard video frame interpolation methods first estimate optical flow between input frames and then synthesize an intermediate frame guided by motion. Recent approaches merge these two steps into a single convolution process by convolving input frames with spatially adaptive kernels that account for motion and re-sampling simultaneously. These methods require large kernels to handle large motion, which limits the number of pixels whose kernels can be estimated at once due to the large memory demand. To address this problem, this paper formulates frame interpolation as local separable convolution over input frames using pairs of 1D kernels. Compared to regular 2D kernels, the 1D kernels require significantly fewer parameters to be estimated. Our method develops a deep fully convolutional neural network that takes two input frames and estimates pairs of 1D kernels for all pixels simultaneously. Since our method is able to estimate kernels and synthesizes the whole video frame at once, it allows for the incorporation of perceptual loss to train the neural network to produce visually pleasing frames. This deep neural network is trained end-to-end using widely available video data without any human annotation. Both qualitative and quantitative experiments show that our method provides a practical solution to high-quality video frame interpolation.