1,354 research outputs found
Context-aware Synthesis for Video Frame Interpolation
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.Comment: CVPR 2018, http://graphics.cs.pdx.edu/project/ctxsy
Video Frame Interpolation via Adaptive Separable Convolution
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.Comment: ICCV 2017, http://graphics.cs.pdx.edu/project/sepconv
Two-Stream Action Recognition-Oriented Video Super-Resolution
We study the video super-resolution (SR) problem for facilitating video
analytics tasks, e.g. action recognition, instead of for visual quality. The
popular action recognition methods based on convolutional networks, exemplified
by two-stream networks, are not directly applicable on video of low spatial
resolution. This can be remedied by performing video SR prior to recognition,
which motivates us to improve the SR procedure for recognition accuracy.
Tailored for two-stream action recognition networks, we propose two video SR
methods for the spatial and temporal streams respectively. On the one hand, we
observe that regions with action are more important to recognition, and we
propose an optical-flow guided weighted mean-squared-error loss for our
spatial-oriented SR (SoSR) network to emphasize the reconstruction of moving
objects. On the other hand, we observe that existing video SR methods incur
temporal discontinuity between frames, which also worsens the recognition
accuracy, and we propose a siamese network for our temporal-oriented SR (ToSR)
training that emphasizes the temporal continuity between consecutive frames. We
perform experiments using two state-of-the-art action recognition networks and
two well-known datasets--UCF101 and HMDB51. Results demonstrate the
effectiveness of our proposed SoSR and ToSR in improving recognition accuracy.Comment: Accepted to ICCV 2019. Code:
https://github.com/AlanZhang1995/TwoStreamS
Deep Video Color Propagation
Traditional approaches for color propagation in videos rely on some form of
matching between consecutive video frames. Using appearance descriptors, colors
are then propagated both spatially and temporally. These methods, however, are
computationally expensive and do not take advantage of semantic information of
the scene. In this work we propose a deep learning framework for color
propagation that combines a local strategy, to propagate colors frame-by-frame
ensuring temporal stability, and a global strategy, using semantics for color
propagation within a longer range. Our evaluation shows the superiority of our
strategy over existing video and image color propagation methods as well as
neural photo-realistic style transfer approaches.Comment: BMVC 201
Motion Offset for Blur Modeling
Motion blur caused by the relative movement between the camera and the subject is often an undesirable degradation of the image quality. In most conventional deblurring methods, a blur kernel is estimated for image deconvolution. Due to the ill-posed nature, predefined priors are proposed to suppress the ill-posedness. However, these predefined priors can only handle some specific situations. In order to achieve a better deblurring performance on dynamic scene, deep-learning based methods are proposed to learn a mapping function that restore the sharp image from a blurry image. The blur may be implicitly modelled in feature extraction module. However, the blur modelled from the paired dataset cannot be well generalized to some real-world scenes. To summary, an accurate and dynamic blur model that more closely approximates real-world blur is needed.
By revisiting the principle of camera exposure, we can model the blur with the displacements between sharp pixels and the exposed pixel, namely motion offsets. Given specific physical constraints, motion offsets are able to form different exposure trajectories (i.e. linear, quadratic). Compare to conventional blur kernel, our proposed motion offsets are a more rigorous approximation for real-world blur, since they can constitute a non-linear and non-uniform motion field. Through learning from dynamic scene dataset, an accurate and spatial-variant motion offset field is obtained.
With accurate motion information and a compact blur modeling method, we explore the ways of utilizing motion information to facilitate multiple blur-related tasks. By introducing recovered motion offsets, we build up a motion-aware and spatial-variant convolution. For extracting a video clip from a blurry image, motion offsets can provide an explicit (non-)linear motion trajectory for interpolating. We also work towards a better image deblurring performance in real-world scenarios by improving the generalization ability of the deblurring model
Duality based optical flow algorithms with applications
We consider the popular TV-L1 optical flow formulation, and the so-called dual-ity based algorithm for minimizing the TV-L1 energy. The original formulation is extended to allow for vector valued images, and minimization results are given. In addition we consider di↵erent definitions of total variation regulariza-tion, and related formulations of the optical flow problem that may be used with a duality based algorithm. We present a highly optimized algorithmic setup to estimate optical flows, and give five novel applications. The first application is registration of medical images, where X-ray images of di↵erent hands, taken using di↵erent imaging devices are registered using a TV-L1 optical flow algo-rithm. We propose to regularize the input images, using sparsity enhancing regularization of the image gradient to improve registration results. The second application is registration of 2D chromatograms, where registration only have to be done in one of the two dimensions, resulting in a vector valued registration problem with values having several hundred dimensions. We propose a nove
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