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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

Showing 101125 of 741 papers

TitleStatusHype
Many-to-many Splatting for Efficient Video Frame InterpolationCode1
Micro Expression Generation with Thin-plate Spline Motion Model and Face ParsingCode1
DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular VideosCode1
Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging DataCode1
Deep Shutter Unrolling NetworkCode1
Channel Attention Is All You Need for Video Frame InterpolationCode1
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place RecognitionCode1
Motion Vector Extrapolation for Video Object DetectionCode1
DEFLOW: Self-supervised 3D Motion Estimation of Debris FlowCode1
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan SynchronizationCode1
4DComplete: Non-Rigid Motion Estimation Beyond the Observable SurfaceCode1
CNN-based Ego-Motion Estimation for Fast MAV ManeuversCode1
Non-linear Motion Estimation for Video Frame Interpolation using Space-time ConvolutionsCode1
Distracting Downpour: Adversarial Weather Attacks for Motion EstimationCode1
A Multi-spectral Dataset for Evaluating Motion Estimation SystemsCode1
Optimizing 4D Gaussians for Dynamic Scene Video from Single Landscape ImagesCode1
DeepMesh: Mesh-based Cardiac Motion Tracking using Deep LearningCode1
PeLiCal: Targetless Extrinsic Calibration via Penetrating Lines for RGB-D Cameras with Limited Co-visibilityCode1
4D Myocardium Reconstruction with Decoupled Motion and Shape ModelCode1
Motion-compensated MR CINE reconstruction with reconstruction-driven motion estimationCode1
RegHEC: Hand-Eye Calibration via Simultaneous Multi-view Point Clouds Registration of Arbitrary ObjectCode1
Restoration of Non-rigidly Distorted Underwater Images using a Combination of Compressive Sensing and Local Polynomial Image RepresentationsCode1
DeepFaceFlow: In-the-wild Dense 3D Facial Motion EstimationCode1
Robust Self-Supervised LiDAR Odometry via Representative Structure Discovery and 3D Inherent Error ModelingCode1
Deep Orientation Uncertainty Learning based on a Bingham LossCode1
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