31,568 research outputs found
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
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
High-speed Video from Asynchronous Camera Array
This paper presents a method for capturing high-speed video using an
asynchronous camera array. Our method sequentially fires each sensor in a
camera array with a small time offset and assembles captured frames into a
high-speed video according to the time stamps. The resulting video, however,
suffers from parallax jittering caused by the viewpoint difference among
sensors in the camera array. To address this problem, we develop a dedicated
novel view synthesis algorithm that transforms the video frames as if they were
captured by a single reference sensor. Specifically, for any frame from a
non-reference sensor, we find the two temporally neighboring frames captured by
the reference sensor. Using these three frames, we render a new frame with the
same time stamp as the non-reference frame but from the viewpoint of the
reference sensor. Specifically, we segment these frames into super-pixels and
then apply local content-preserving warping to warp them to form the new frame.
We employ a multi-label Markov Random Field method to blend these warped
frames. Our experiments show that our method can produce high-quality and
high-speed video of a wide variety of scenes with large parallax, scene
dynamics, and camera motion and outperforms several baseline and
state-of-the-art approaches.Comment: 10 pages, 82 figures, Published at IEEE WACV 201
Stochastic Dynamics for Video Infilling
In this paper, we introduce a stochastic dynamics video infilling (SDVI)
framework to generate frames between long intervals in a video. Our task
differs from video interpolation which aims to produce transitional frames for
a short interval between every two frames and increase the temporal resolution.
Our task, namely video infilling, however, aims to infill long intervals with
plausible frame sequences. Our framework models the infilling as a constrained
stochastic generation process and sequentially samples dynamics from the
inferred distribution. SDVI consists of two parts: (1) a bi-directional
constraint propagation module to guarantee the spatial-temporal coherence among
frames, (2) a stochastic sampling process to generate dynamics from the
inferred distributions. Experimental results show that SDVI can generate clear
frame sequences with varying contents. Moreover, motions in the generated
sequence are realistic and able to transfer smoothly from the given start frame
to the terminal frame. Our project site is
https://xharlie.github.io/projects/project_sites/SDVI/video_results.htmlComment: Winter Conference on Applications of Computer Vision (WACV 2020
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