Repository: hzwer/Awesome-Optical-Flow Branch: main Commit: b036c804ed25 Files: 1 Total size: 26.8 KB Directory structure: gitextract_i4n26g6h/ └── README.md ================================================ FILE CONTENTS ================================================ ================================================ FILE: README.md ================================================ # Awesome-Optical-Flow This is a list of awesome articles about optical flow and related work. [Click here to read in full screen.](https://github.com/hzwer/Awesome-Optical-Flow/blob/main/README.md) The table of contents is on the right side of the "README.md". Recently, I write [A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches](https://arxiv.org/pdf/2401.14718 ), welcome to read. ## Optical Flow ### Supervised Models | Time | Paper | Repo | | -------- | -------- | -------- | |CVPR24|[MemFlow: Optical Flow Estimation and Prediction with Memory](https://dqiaole.github.io/MemFlow/)|[MemFlow](https://github.com/DQiaole/MemFlow) | |CVPR23|[DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling](https://arxiv.org/abs/2303.14078) |CVPR23|[Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation](https://openaccess.thecvf.com/content/CVPR2023/html/Shi_FlowFormer_Masked_Cost_Volume_Autoencoding_for_Pretraining_Optical_Flow_Estimation_CVPR_2023_paper.html)|[FlowFormerPlusPlus](https://github.com/XiaoyuShi97/FlowFormerPlusPlus) | |NeurIPS22|[SKFlow: Learning Optical Flow with Super Kernels](https://openreview.net/forum?id=v2es9YoukWO)|[SKFlow](https://github.com/littlespray/SKFlow) | |ECCV22|[Disentangling architecture and training for optical flow](https://arxiv.org/abs/2203.10712)|[Autoflow](https://github.com/google-research/opticalflow-autoflow) | |ECCV22|[FlowFormer: A Transformer Architecture for Optical Flow](https://arxiv.org/pdf/2203.16194.pdf)|[FlowFormer](https://github.com/drinkingcoder/FlowFormer-Official/) | |CVPR22|[Learning Optical Flow with Kernel Patch Attention](https://openaccess.thecvf.com/content/CVPR2022/papers/Luo_Learning_Optical_Flow_With_Kernel_Patch_Attention_CVPR_2022_paper.pdf)|[KPAFlow](https://github.com/megvii-research/KPAFlow) | |CVPR22|[GMFlow: Learning Optical Flow via Global Matching](https://arxiv.org/abs/2111.13680)|[gmflow](https://github.com/haofeixu/gmflow) | |CVPR22|[Deep Equilibrium Optical Flow Estimation](https://arxiv.org/pdf/2204.08442.pdf)|[deq-flow](https://github.com/locuslab/deq-flow) | |ICCV21|[High-Resolution Optical Flow from 1D Attention and Correlation](https://arxiv.org/abs/2104.13918)|[flow1d](https://github.com/haofeixu/flow1d)| |ICCV21|[Learning to Estimate Hidden Motions with Global Motion Aggregation](https://arxiv.org/abs/2104.02409)|[GMA](https://github.com/zacjiang/GMA) | |CVPR21|[Learning Optical Flow from a Few Matches](https://arxiv.org/abs/2104.02166)|[SCV](https://github.com/zacjiang/SCV) | |TIP21|[Detail Preserving Coarse-to-Fine Matching for Stereo Matching and Optical Flow](https://ieeexplore.ieee.org/document/9459444) |ECCV20|[RAFT: Recurrent All Pairs Field Transforms for Optical Flow](https://arxiv.org/pdf/2003.12039.pdf)|[RAFT](https://github.com/princeton-vl/RAFT)  |CVPR20|[MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask](https://arxiv.org/abs/2003.10955)|[MaskFlownet](https://github.com/microsoft/MaskFlownet)  |CVPR20|[ScopeFlow: Dynamic Scene Scoping for Optical Flow](https://arxiv.org/abs/2002.10770)|[ScopeFlow](https://github.com/avirambh/ScopeFlow)  |TPAMI20|[A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization](https://arxiv.org/abs/1903.07414)|[LiteFlowNet2](https://github.com/twhui/LiteFlowNet2)  ### Multi-Frame Supervised Models | Time | Paper | Repo | | -------- | -------- | -------- | |ECCV24|[Local All-Pair Correspondence for Point Tracking](https://arxiv.org/abs/2407.15420) |CVPR24|[FlowTrack: Revisiting Optical Flow for Long-Range Dense Tracking](https://openaccess.thecvf.com/content/CVPR2024/html/Cho_FlowTrack_Revisiting_Optical_Flow_for_Long-Range_Dense_Tracking_CVPR_2024_paper.html) |CVPR24|[Dense Optical Tracking: Connecting the Dots](https://arxiv.org/abs/2312.00786)|[dot](https://github.com/16lemoing/dot) | |ICCV23|[Tracking Everything Everywhere All at Once](https://arxiv.org/abs/2306.05422)|[omnimotion](https://github.com/qianqianwang68/omnimotion) | |ICCV23|[AccFlow: Backward Accumulation for Long-Range Optical Flow](https://arxiv.org/pdf/2308.13133.pdf)|[AccFlow](https://github.com/mulns/AccFlow) | |ICCV23|[VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation](https://arxiv.org/abs/2303.08340)|[VideoFlow](https://github.com/XiaoyuShi97/VideoFlow) | |ECCV22|[Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories](https://arxiv.org/abs/2204.04153)|[PIPs](https://github.com/aharley/pips) | ### Semi-Supervised Models | Time | Paper | Repo | | -------- | -------- | -------- | |ECCV22|[Semi-Supervised Learning of Optical Flow by Flow Supervisor](https://arxiv.org/abs/2207.10314) ### Data Synthesis | Time | Paper | Repo | | -------- | -------- | -------- | |ECCV22|[RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos]()|[RealFlow](https://github.com/megvii-research/RealFlow)  |CVPR21|[AutoFlow: Learning a Better Training Set for Optical Flow](https://arxiv.org/abs/2104.14544)|[autoflow](https://github.com/google-research/opticalflow-autoflow)  |CVPR21|[Learning Optical Flow from Still Images](https://arxiv.org/abs/2104.03965)|[depthstillation](https://github.com/mattpoggi/depthstillation)  |arXiv21.04|[Optical Flow Dataset Synthesis from Unpaired Images](https://arxiv.org/abs/2104.02615) ### Unsupervised Models | Time | Paper | Repo | | -------- | -------- | -------- | |ECCV22|[Optical Flow Training under Limited Label Budget via Active Learning](https://arxiv.org/pdf/2203.05053.pdf)|[optical-flow-active-learning-release](https://github.com/duke-vision/optical-flow-active-learning-release)  |CVPR21|[SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping](https://arxiv.org/abs/2105.07014)|[smurf](https://github.com/google-research/google-research/tree/master/smurf) GoogleResearch |CVPR21|[UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning](https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_UPFlow_Upsampling_Pyramid_for_Unsupervised_Optical_Flow_Learning_CVPR_2021_paper.pdf)|[UPFlow_pytorch](https://github.com/coolbeam/UPFlow_pytorch)  |TIP21|[OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning](https://arxiv.org/abs/2006.16637)|[depthstillation](https://github.com/coolbeam/OIFlow)  |ECCV20|[What Matters in Unsupervised Optical Flow](https://arxiv.org/abs/2006.04902)|[uflow](https://github.com/google-research/google-research/tree/master/uflow) GoogleResearch |CVPR20|[Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation](https://arxiv.org/abs/2003.13045)|[ARFlow](https://github.com/lliuz/ARFlow)  |CVPR20|[Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching](https://arxiv.org/abs/2004.02138) ### Joint Learning | Time | Paper | Repo | | -------- | -------- | -------- | |arXiv21.11|[Unifying Flow, Stereo and Depth Estimation](https://arxiv.org/abs/2211.05783)|[unimatch](https://github.com/autonomousvision/unimatch) | |CVPR21|[EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation](https://openaccess.thecvf.com/content/CVPR2021/html/Jiao_EffiScene_Efficient_Per-Pixel_Rigidity_Inference_for_Unsupervised_Joint_Learning_of_CVPR_2021_paper.html) |CVPR21|[Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion](https://openaccess.thecvf.com/content/CVPR2021/html/Chi_Feature-Level_Collaboration_Joint_Unsupervised_Learning_of_Optical_Flow_Stereo_Depth_CVPR_2021_paper.html) ### Special Scene | Time | Paper | Repo | | -------- | -------- | -------- | |CVPR23|[Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow](https://arxiv.org/abs/2303.07564) |[UCDA-Flow](https://github.com/hyzhouboy/UCDA-Flow)  |ECCV22|[Deep 360∘ Optical Flow Estimation Based on Multi-Projection Fusion](https://arxiv.org/abs/2208.00776) |AAAI21|[Optical flow estimation from a single motion-blurred image](https://www.aaai.org/AAAI21Papers/AAAI-3339.ArgawD.pdf)| |CVPR20|[Optical Flow in Dense Foggy Scenes using Semi-Supervised Learning](https://arxiv.org/abs/2004.01905) |CVPR20|[Optical Flow in the Dark](https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Optical_Flow_in_the_Dark_CVPR_2020_paper.html)|[Optical-Flow-in-the-Dark](https://github.com/mf-zhang/Optical-Flow-in-the-Dark)  ### Special Device **Event Camera** [event-based_vision_resources](https://github.com/uzh-rpg/event-based_vision_resources#optical-flow-estimation)  | Time | Paper | Repo | | -------- | -------- | -------- | |ArXiv23.03|[Learning Optical Flow from Event Camera with Rendered Dataset](https://arxiv.org/abs/2303.11011) |ECCV22|[Secrets of Event-Based Optical Flow](https://arxiv.org/abs/2207.10022)|[event_based_optical_flow](https://github.com/tub-rip/event_based_optical_flow)  |ICCV21|[GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning](https://arxiv.org/abs/2103.13725)|[GyroFlow](https://github.com/megvii-research/GyroFlow)  ## Scene Flow | Time | Paper | Repo | | -------- | -------- | -------- | |CVPR21|[RAFT-3D: Scene Flow Using Rigid-Motion Embeddings](https://arxiv.org/pdf/2012.00726.pdf) |CVPR21|[Just Go With the Flow: Self-Supervised Scene Flow Estimation](https://arxiv.org/pdf/1912.00497.pdf)|[Just-Go-with-the-Flow-Self-Supervised-Scene-Flow-Estimation](https://github.com/HimangiM/Just-Go-with-the-Flow-Self-Supervised-Scene-Flow-Estimation)  |CVPR21|[Learning to Segment Rigid Motions from Two Frames](https://arxiv.org/abs/2101.03694)|[rigidmask](https://github.com/gengshan-y/rigidmask) |CVPR20|[Upgrading Optical Flow to 3D Scene Flow through Optical Expansion](https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Upgrading_Optical_Flow_to_3D_Scene_Flow_Through_Optical_Expansion_CVPR_2020_paper.html)|[expansion](https://github.com/gengshan-y/expansion)  |CVPR20|[Self-Supervised Monocular Scene Flow Estimation](https://arxiv.org/abs/2004.04143)|[self-mono-sf](https://github.com/visinf/self-mono-sf)  ## Applications ### Video Synthesis/Generation | Time | Paper | Repo | | -------- | -------- | -------- |ECCV24|[Clearer Frames, Anytime: Resolving Velocity Ambiguity in Video Frame Interpolation](https://jianwang-cmu.github.io/23VFI/04908.pdf)|[InterpAny-Clearer](https://github.com/zzh-tech/InterpAny-Clearer)  |arXiv23.11|[MoVideo: Motion-Aware Video Generation with Diffusion Models](https://arxiv.org/abs/2311.11325) |CVPR24|[FlowVid: Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesis](https://arxiv.org/pdf/2312.17681.pdf) |WACV24|[Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution](https://arxiv.org/abs/2310.17294)|[SAFA](https://github.com/megvii-research/WACV2024-SAFA)  |CVPR23|[A Dynamic Multi-Scale Voxel Flow Network for Video Prediction](https://arxiv.org/abs/2303.09875)|[DMVFN](https://github.com/megvii-research/CVPR2023-DMVFN)  |CVPR23|[Conditional Image-to-Video Generation with Latent Flow Diffusion Models](https://openaccess.thecvf.com/content/CVPR2023/papers/Ni_Conditional_Image-to-Video_Generation_With_Latent_Flow_Diffusion_Models_CVPR_2023_paper.pdf)|[LFDM](https://github.com/nihaomiao/CVPR23_LFDM)  |CVPR23|[A Unified Pyramid Recurrent Network for Video Frame Interpolation](https://arxiv.org/abs/2211.03456)|[UPR-Net](https://github.com/srcn-ivl/UPR-Net)  |CVPR23|[Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation](https://arxiv.org/abs/2303.00440)|[EMA-VFI](https://github.com/MCG-NJU/EMA-VFI)  |WACV23|[Frame Interpolation for Dynamic Scenes with Implicit Flow Encoding](https://openaccess.thecvf.com/content/WACV2023/papers/Figueiredo_Frame_Interpolation_for_Dynamic_Scenes_With_Implicit_Flow_Encoding_WACV_2023_paper.pdf)|[frameintIFE](https://github.com/pedrovfigueiredo/frameintIFE)  |ACMMM22|[Neighbor correspondence matching for flow-based video frame synthesis](https://arxiv.org/abs/2207.06763)| |ECCV22|[Improving the Perceptual Quality of 2D Animation Interpolation](https://arxiv.org/abs/2011.06294)|[eisai](https://github.com/ShuhongChen/eisai-anime-interpolator)  |ECCV22|[Real-Time Intermediate Flow Estimation for Video Frame Interpolation](https://arxiv.org/abs/2011.06294)|[RIFE](https://github.com/hzwer/ECCV2022-RIFE)  |CVPR22|[VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution](https://arxiv.org/pdf/2206.04647.pdf)|[VideoINR](https://github.com/Picsart-AI-Research/VideoINR-Continuous-Space-Time-Super-Resolution)  |CVPR22|[IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation](https://arxiv.org/pdf/2205.14620.pdf)|[IFRNet](https://github.com/ltkong218/IFRNet)  |TOG21|[Neural Frame Interpolation for Rendered Content](https://dl.acm.org/doi/abs/10.1145/3478513.3480553) |CVPR21|[Deep Animation Video Interpolation in the Wild](https://arxiv.org/abs/2104.02495)|[AnimeInterp](https://github.com/lisiyao21/AnimeInterp)  |CVPR20|[Softmax Splatting for Video Frame Interpolation](https://arxiv.org/abs/2003.05534)|[softmax-splatting](https://github.com/sniklaus/softmax-splatting)  |CVPR20|[Adaptive Collaboration of Flows for Video Frame Interpolation](https://arxiv.org/abs/1907.10244)|[AdaCoF-pytorch](https://github.com/HyeongminLEE/AdaCoF-pytorch)  |CVPR20|[FeatureFlow: Robust Video Interpolation via Structure-to-Texture Generation](https://openaccess.thecvf.com/content_CVPR_2020/papers/Gui_FeatureFlow_Robust_Video_Interpolation_via_Structure-to-Texture_Generation_CVPR_2020_paper.pdf)|[FeatureFlow](https://github.com/CM-BF/FeatureFlow)  ### Video Inpainting | Time | Paper | Repo | | -------- | -------- | -------- | |ECCV22|[Flow-Guided Transformer for Video Inpainting](https://arxiv.org/abs/2208.06768)|[FGT](https://github.com/hitachinsk/FGT)  |CVPR22|[Inertia-Guided Flow Completion and Style Fusion for Video Inpainting](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Inertia-Guided_Flow_Completion_and_Style_Fusion_for_Video_Inpainting_CVPR_2022_paper.pdf)|[isvi](https://github.com/hitachinsk/isvi)  ### Video Stabilization | Time | Paper | Repo | | -------- | -------- | -------- | |CVPR20|[Learning Video Stabilization Using Optical Flow](https://cseweb.ucsd.edu/~ravir/jiyang_cvpr20.pdf)|[jiyang.fun](https://drive.google.com/file/d/1wQJYFd8TMbCRzhmFfDyBj7oHAGfyr1j6/view) ### Low Level Vision | Time | Paper | Repo | | -------- | -------- | -------- | |ICCV21|[Deep Reparametrization of Multi-Frame Super-Resolution and Denoising](https://arxiv.org/abs/2108.08286)|[deep-rep](https://github.com/goutamgmb/deep-rep)  |CVPR21|[Deep Burst Super-Resolution](https://arxiv.org/abs/2101.10997)|[deep-burst-sr](https://github.com/goutamgmb/deep-burst-sr)  |CVPR20|[Efficient Dynamic Scene Deblurring Using Spatially Variant Deconvolution Network With Optical Flow Guided Training](https://openaccess.thecvf.com/content_CVPR_2020/html/Yuan_Efficient_Dynamic_Scene_Deblurring_Using_Spatially_Variant_Deconvolution_Network_With_CVPR_2020_paper.html)| |TIP20|[Deep video super-resolution using HR optical flow estimation](https://arxiv.org/abs/2001.02129)|[SOF-VSR](https://github.com/The-Learning-And-Vision-Atelier-LAVA/SOF-VSR)  ### Stereo and SLAM | Time | Paper | Repo | | -------- | -------- | -------- | |3DV21|[RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://arxiv.org/pdf/2109.07547.pdf)|[RAFT-Stereo](https://github.com/princeton-vl/RAFT-Stereo)  |CVPR20|[VOLDOR: Visual Odometry From Log-Logistic Dense Optical Flow Residuals](https://openaccess.thecvf.com/content_CVPR_2020/html/Min_VOLDOR_Visual_Odometry_From_Log-Logistic_Dense_Optical_Flow_Residuals_CVPR_2020_paper.html)|[VOLDOR](https://github.com/htkseason/VOLDOR)  ## Before 2020 ### Classical Estimation Methods | Time | Paper | Repo | | -------- | -------- | -------- | |IJCAI1981|[An iterative image registration technique with an application to stereo vision](http://citeseer.ist.psu.edu/viewdoc/download;jsessionid=C41563DCDDC44CB0E13D6D64D89FF3FD?doi=10.1.1.421.4619&rep=rep1&type=pdf)|| |AI1981|[Determining optical flow](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.66.562&rep=rep1&type=pdf)| |TPAMI10|[Motion Detail Preserving Optical Flow Estimation](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.221.896&rep=rep1&type=pdf) |CVPR10|[Secrets of Optical Flow Estimation and Their Principles](https://users.soe.ucsc.edu/~pang/200/f18/papers/2018/05539939.pdf) |ICCV13|[DeepFlow: Large Displacement Optical Flow with Deep Matching](https://openaccess.thecvf.com/content_iccv_2013/papers/Weinzaepfel_DeepFlow_Large_Displacement_2013_ICCV_paper.pdf)|[Project](https://thoth.inrialpes.fr/src/deepflow/) |ECCV14|[Optical Flow Estimation with Channel Constancy](https://link.springer.com/content/pdf/10.1007/978-3-319-10590-1_28.pdf) |CVPR17|[S2F: Slow-To-Fast Interpolator Flow](https://openaccess.thecvf.com/content_cvpr_2017/papers/Yang_S2F_Slow-To-Fast_Interpolator_CVPR_2017_paper.pdf) ### Others | Time | Paper | Repo | | -------- | -------- | -------- | |NeurIPS19|[Volumetric Correspondence Networks for Optical Flow](https://papers.nips.cc/paper/2019/hash/bbf94b34eb32268ada57a3be5062fe7d-Abstract.html)|[VCN](https://github.com/gengshan-y/VCN)  |CVPR19|[Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation](https://arxiv.org/pdf/1904.05290.pdf)|[irr](https://github.com/visinf/irr)  |CVPR18|[PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://arxiv.org/abs/1709.02371)|[PWC-Net](https://github.com/NVlabs/PWC-Net)  | [pytorch-pwc](https://github.com/sniklaus/pytorch-pwc)  |CVPR18|[LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation](https://arxiv.org/abs/1805.07036)|[LiteFlowNet](https://github.com/twhui/LiteFlowNet)  | [pytorch-liteflownet](https://github.com/sniklaus/pytorch-liteflownet)  |CVPR17|[FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://arxiv.org/abs/1612.01925)|[flownet2-pytorch](https://github.com/NVIDIA/flownet2-pytorch)  <br> [flownet2](https://github.com/lmb-freiburg/flownet2)  <br> [flownet2-tf](https://github.com/sampepose/flownet2-tf)  |CVPR17|[Optical Flow Estimation using a Spatial Pyramid Network](https://arxiv.org/abs/1611.00850)|[spynet](https://github.com/anuragranj/spynet)  | [pytorch-spynet](https://github.com/sniklaus/pytorch-spynet)  |ICCV15|[FlowNet: Learning Optical Flow with Convolutional Networks](https://arxiv.org/abs/1504.06852)|[FlowNetPytorch](https://github.com/ClementPinard/FlowNetPytorch)  |AAAI19|[DDFlow: Learning Optical Flow with Unlabeled Data Distillation](https://arxiv.org/abs/1902.09145)|[DDFlow](https://github.com/ppliuboy/DDFlow)  |CVPR19|[SelFlow: Self-Supervised Learning of Optical Flow](https://arxiv.org/abs/1904.09117)|[SelFlow](https://github.com/ppliuboy/SelFlow)  |CVPR19|[Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes](https://arxiv.org/abs/1904.03848)|[EPIFlow](https://github.com/yiranzhong/EPIflow)  |CVPR18|[Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose](https://arxiv.org/abs/1803.02276)|[GeoNet](https://github.com/yzcjtr/GeoNet)  |ICCV19|[RainFlow: Optical Flow under Rain Streaks and Rain Veiling Effect](https://openaccess.thecvf.com/content_ICCV_2019/html/Li_RainFlow_Optical_Flow_Under_Rain_Streaks_and_Rain_Veiling_Effect_ICCV_2019_paper.html) |CVPR18|[Robust Optical Flow Estimation in Rainy Scenes](https://arxiv.org/abs/1704.05239) |NIPS19|[Quadratic Video Interpolation](https://arxiv.org/abs/1911.00627) |CVPR19|[Depth-Aware Video Frame Interpolation](https://openaccess.thecvf.com/content_CVPR_2019/papers/Bao_Depth-Aware_Video_Frame_Interpolation_CVPR_2019_paper.pdf)|[DAIN](https://github.com/baowenbo/DAIN)  |CVPR18|[Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://arxiv.org/abs/1712.00080)|[Super-SloMo](https://github.com/avinashpaliwal/Super-SloMo)  |ICCV17|[Video Frame Synthesis using Deep Voxel Flow](https://arxiv.org/abs/1702.02463)|[voxel-flow](https://github.com/liuziwei7/voxel-flow)  | [pytorch-voxel-flow](https://github.com/lxx1991/pytorch-voxel-flow)  |CVPR19|[DVC: An End-to-end Deep Video Compression Framework](https://arxiv.org/abs/1812.00101)|[PyTorchVideoCompression](https://github.com/ZhihaoHu/PyTorchVideoCompression)  |ICCV17|[SegFlow: Joint Learning for Video Object Segmentation and Optical Flow](https://arxiv.org/abs/1709.06750)|[SegFlow](https://github.com/JingchunCheng/SegFlow)  |CVPR18|[End-to-end Flow Correlation Tracking with Spatial-temporal Attention](https://arxiv.org/abs/1711.01124) |CVPR18|[Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition](https://arxiv.org/abs/1711.11152)|[Optical-Flow-Guided-Feature](https://github.com/kevin-ssy/Optical-Flow-Guided-Feature)  |GCPR18|[On the Integration of Optical Flow and Action Recognition](https://arxiv.org/abs/1712.08416) |CVPR14|[Spatially Smooth Optical Flow for Video Stabilization](http://www.liushuaicheng.org/CVPR2014/SteadyFlow.pdf)
gitextract_i4n26g6h/ └── README.md
Condensed preview — 1 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (28K chars).
[
{
"path": "README.md",
"chars": 27494,
"preview": "# Awesome-Optical-Flow\nThis is a list of awesome articles about optical flow and related work. [Click here to read in fu"
}
]
About this extraction
This page contains the full source code of the hzwer/Awesome-Optical-Flow GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 1 files (26.8 KB), approximately 8.6k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.