https://github.com/ryersonvisionlab/two-stream-dyntex-synth
Two-Stream Convolutional Networks for Dynamic Texture Synthesis
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/sailab-code/SAILenv
SAILenv: Learning in Virtual Visual Environments Made Simple
https://github.com/sair-lab/ni-slam
Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators
https://github.com/sczhou/STFAN
Spatio-Temporal Filter Adaptive Network for Video Deblurring
https://github.com/shashankvkt/video_object_segmentation
https://github.com/shayzweig/InterpoNet
InterpoNet, A brain inspired neural network for optical flow dense interpolation
https://github.com/shobrook/sequitur
Beyond Short Snippets: Deep Networks for Video Classification
https://github.com/shuuchen/PoseWarper.pytorch
Learning Temporal Pose Estimation from Sparsely-Labeled Videos
https://github.com/sid-roheda/Volterra-Neural-Networks
https://github.com/sithu31296/semantic-segmentation
https://github.com/sniklaus/pytorch-liteflownet
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
https://github.com/sniklaus/pytorch-pwc
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
https://github.com/sniklaus/pytorch-spynet
https://github.com/sniklaus/pytorch-unflow
UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss
https://github.com/soCzech/TransNetV2
https://github.com/sonnguyentruong129/Real-time-Style-Transfer
https://github.com/stfc-sciml/differentialconv2d
https://github.com/sunghoonim/DPSNet
https://github.com/susomena/DeepSlowMotion
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
https://github.com/sxjscience/HKO-7
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
https://github.com/synce1234/FTGAN_custom
Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture
https://github.com/tehret/vbm3d
Implementation of the VBM3D Video Denoising Method and Some Variants
https://github.com/thanosvlo/Deep-Segmentation-and-Registration-in-X-Ray-Angiography-Video
Deep Segmentation and Registration in X-Ray Angiography Video
https://github.com/tianhai123/conv-gru
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
https://github.com/tianyuan168326/EAN-Pytorch
PAN: Towards Fast Action Recognition via Learning Persistence of Appearance
https://github.com/ttengwang/ESGN
Fast Learning of Temporal Action Proposal via Dense Boundary Generator
https://github.com/tub-rip/dvs_global_flow_skeleton
https://github.com/umutsuluhan/HSTRNet-Interp
HSTR-Net: High Spatio-Temporal Resolution Video Generation For Wide Area Surveillance
https://github.com/uncbiag/InverseConsistency
$\texttt{GradICON}$: Approximate Diffeomorphisms via Gradient Inverse Consistency
https://github.com/uzh-rpg/deep_ev_tracker
https://github.com/verlab/SemanticFastForward_CVPR_2018
https://github.com/verlab/SemanticFastForward_TPAMI_2020
https://github.com/vikramjit-sidhu/hlcv_project
Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture
https://github.com/vlis2022/awesome-image-prior
https://github.com/vwegn/dm
https://github.com/wh1h/DNLN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.