https://github.com/egkang-luis/burstm
공식 코드BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical Flow
Papers with Code 아카이브
Optical Flow Estimation
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Optical Flow Estimation is a computer vision task that involves computing the motion of objects in an image or a video sequence. The goal of optical flow estimation is to determine the movement of pixels or features in the image, which can be used for various applications such as object tracking, motion analysis, and video compression.
Approaches for optical flow estimation include correlation-based, block-matching, feature tracking, energy-based, and more recently gradient-based.
Further readings:
Definition source: Devon: Deformable Volume Network for Learning Optical Flow
Image credit: Optical Flow Estimation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/egocentricvision/n-epic-kitchens
공식 코드E$^2$(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition
https://github.com/emmt1998/neuralvelocimetry
공식 코드Image Velocimetry using Direct Displacement Field estimation with Neural Networks for Fluids
https://github.com/endo-yuki-t/UserControllableLT
공식 코드User-Controllable Latent Transformer for StyleGAN Image Layout Editing
https://github.com/erizmr/UnLiteFlowNet-PIV
공식 코드https://github.com/erizmr/learn-to-estimate-fluid-motion
공식 코드Learning to Estimate and Refine Fluid Motion with Physical Dynamics
https://github.com/ethliup/SelfDeblur
공식 코드https://github.com/eunjnnn/bfstvsr
공식 코드https://github.com/eventbasedvision/sevd
공식 코드SEVD: Synthetic Event-based Vision Dataset for Ego and Fixed Traffic Perception
https://github.com/eye-tracking-for-physiological-sensing/eyelorin
공식 코드https://github.com/eyeline-research/go-with-the-flow
공식 코드Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise
https://github.com/f-ilic/AppearanceFreeActionRecognition
공식 코드https://github.com/facebookresearch/PoseWarper
공식 코드Learning Temporal Pose Estimation from Sparsely-Labeled Videos
https://github.com/facebookresearch/co-tracker
공식 코드https://github.com/facebookresearch/cutler
공식 코드VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation
https://github.com/facebookresearch/unsamflow
공식 코드UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model
https://github.com/facias914/A-Simple-Detector-is-a-Strong-Tracker
공식 코드https://github.com/fangli333/sc-4dgs
공식 코드Self-Calibrating 4D Novel View Synthesis from Monocular Videos Using Gaussian Splatting
https://github.com/fangyini/rmes
공식 코드RMES: Real-Time Micro-Expression Spotting Using Phase From Riesz Pyramid
https://github.com/feichtenhofer/twostreamfusion
공식 코드Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/feinanshan/m2m_vfi
공식 코드Many-to-many Splatting for Efficient Video Frame Interpolation
https://github.com/feipanir/moda
공식 코드https://github.com/fisseha21/z-upscaling
공식 코드Z-upscaling: Optical Flow Guided Frame Interpolation for Isotropic Reconstruction of 3D EM Volumes
https://github.com/fmidev/cloudcast
공식 코드CloudCast -- Total Cloud Cover Nowcasting with Machine Learning
https://github.com/fpv-iplab/rulstm
공식 코드https://github.com/french-paragon/libstevi
공식 코드Generalized Closed-form Formulae for Feature-based Subpixel Alignment in Patch-based Matching
https://github.com/fzi-forschungszentrum-informatik/NNAD
공식 코드https://github.com/gallif/costfunctionunrolling
공식 코드https://github.com/gangweix/hcvflow
공식 코드https://github.com/gangweix/meflow
공식 코드Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost Volume
https://github.com/genbing99/SoftNet-SpotME
공식 코드Shallow Optical Flow Three-Stream CNN for Macro- and Micro-Expression Spotting from Long Videos
https://github.com/giddyyupp/wait
공식 코드WAIT: Feature Warping for Animation to Illustration video Translation using GANs
https://github.com/gitcvfb/rssr
공식 코드Rolling Shutter Inversion: Bring Rolling Shutter Images to High Framerate Global Shutter Video
https://github.com/globalflownet/globalflownet
공식 코드GlobalFlowNet: Video Stabilization using Deep Distilled Global Motion Estimates
https://github.com/google-deepmind/tapnet
공식 코드https://github.com/google-research/frame-interpolation
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.