Video Reconstruction
35 papers with code • 9 benchmarks • 8 datasets
Source: Deep-SloMo
Benchmarks
These leaderboards are used to track progress in Video Reconstruction
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VoxCeleb1
Event-Camera Dataset
MVSEC
MGif
TED-talks
Tai-Chi-HD
SEN12MS-CR-TS
Videezy4K
Most implemented papers
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
MCG-NJU/VideoMAE
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Pre-training video transformers on extra large-scale datasets is generally required to achieve premier performance on relatively small datasets.
NeRV: Neural Representations for Videos
haochen-rye/nerv
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NeurIPS 2021
In contrast, with NeRV, we can use any neural network compression method as a proxy for video compression, and achieve comparable performance to traditional frame-based video compression approaches (H. 264, HEVC \etc).
First Order Motion Model for Image Animation
AliaksandrSiarohin/first-order-model
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NeurIPS 2019
To achieve this, we decouple appearance and motion information using a self-supervised formulation.
Motion Representations for Articulated Animation
snap-research/articulated-animation
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CVPR 2021
To facilitate animation and prevent the leakage of the shape of the driving object, we disentangle shape and pose of objects in the region space.
Layered Neural Atlases for Consistent Video Editing
ykasten/layered-neural-atlases
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We present a method that decomposes, or "unwraps", an input video into a set of layered 2D atlases, each providing a unified representation of the appearance of an object (or background) over the video.
Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time
Moreover, on the seemingly implausible x16 interpolation task, our method outperforms existing methods by more than 1. 5 dB in terms of PSNR.
DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing
In this paper, we propose a novel encoder-decoder neural network model referred to as DeepBinaryMask for video compressive sensing.
Bringing Alive Blurred Moments
This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder.
Exploiting Structure for Fast Kernel Learning
We propose two methods for exact Gaussian process (GP) inference and learning on massive image, video, spatial-temporal, or multi-output datasets with missing values (or "gaps") in the observed responses.
High Frame Rate Video Reconstruction based on an Event Camera
Based on the abundant event data alongside a low frame rate, easily blurred images, we propose a simple yet effective approach to reconstruct high-quality and high frame rate sharp videos.
