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Motion Estimation

Motion Estimation is used to determine the block-wise or pixel-wise motion vectors between two frames.

Source: MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement

Papers

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Showing 476–500 of 741 papers

TitleStatusHype
A 3D Motion Vector Database for Dynamic Point Clouds—0
Mesh Guided One-shot Face Reenactment using Graph Convolutional Networks—0
Anatomy-Aware Cardiac Motion Estimation—0
End-to-End Rate-Distortion Optimization for Bi-Directional Learned Video Compression—0
Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection—0
Polarized Optical-Flow Gyroscope—0
Perpetual Motion: Generating Unbounded Human Motion—0
Using modern motion estimation algorithms in existing video codecs—0
Minimal Cases for Computing the Generalized Relative Pose using Affine Correspondences—0
Real-time Dense Reconstruction of Tissue Surface from Stereo Optical Video—0
Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge?—0
Monitoring of Critical Infrastructures by Micro-Motion Estimation: the Mosul Dam Destabilization—0
Patient-Specific Domain Adaptation for Fast Optical Flow Based on Teacher-Student Knowledge Transfer—0
IMU Preintegrated Features for Efficient Deep Inertial Odometry—0
Directional Primitives for Uncertainty-Aware Motion Estimation in Urban Environments—0
Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation—0
Appearance Learning for Image-based Motion Estimation in Tomography—0
Adaptively Meshed Video Stabilization—0
Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation—0
Patch based Colour Transfer using SIFT Flow—0
Taskology: Utilizing Task Relations at Scale—0
Streaming Object Detection for 3-D Point Clouds—0
Tamed Warping Network for High-Resolution Semantic Video Segmentation—0
Self-Supervised Attention Learning for Depth and Ego-motion Estimation—0
A Deep Learning Approach for Motion Forecasting Using 4D OCT Data—0
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