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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/J911/MISO-VFI
공식 코드A Multi-In-Single-Out Network for Video Frame Interpolation without Optical Flow
https://github.com/JHang2020/HiCLR
공식 코드https://github.com/JHang2020/Shap-Mix
공식 코드Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition
https://github.com/JOY-MM/JoyGen
공식 코드JoyGen: Audio-Driven 3D Depth-Aware Talking-Face Video Editing
https://github.com/JaeYeonKang/STVUN-Pytorch
공식 코드https://github.com/JarrentWu1031/CCPL
공식 코드CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer
https://github.com/Jason-Qiu/MultiSum_model
공식 코드MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos
https://github.com/Jeckinchen/PhyCoBench
공식 코드https://github.com/JerryYLi/Dataset-REPAIR
공식 코드https://github.com/Jhhuangkay/Query-controllable-Video-Summarization
공식 코드https://github.com/Jho-Yonsei/HD-GCN
공식 코드Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/Jho-Yonsei/STC-Net
공식 코드Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognition
https://github.com/Ji4chenLi/t2v-turbo
공식 코드T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback
https://github.com/Jiamian-Wang/HSI_baseline
공식 코드A Simple and Efficient Reconstruction Backbone for Snapshot Compressive Imaging
https://github.com/JianZhang-chick/EMOdiffhead
공식 코드EMOdiffhead: Continuously Emotional Control in Talking Head Generation via Diffusion
https://github.com/JiangkaiWu/Promptus
공식 코드Promptus: Can Prompts Streaming Replace Video Streaming with Stable Diffusion
https://github.com/JianpingLin/M-LVC_CVPR2020
공식 코드M-LVC: Multiple Frames Prediction for Learned Video Compression
https://github.com/JihyongOh/DeMFI
공식 코드https://github.com/JihyongOh/FISR
공식 코드FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal Loss
https://github.com/JihyongOh/XVFI
공식 코드https://github.com/JingchunCheng/FAVOS
공식 코드Fast and Accurate Online Video Object Segmentation via Tracking Parts
https://github.com/JingchunCheng/SegFlow
공식 코드SegFlow: Joint Learning for Video Object Segmentation and Optical Flow
https://github.com/JiwanChung/long-story-short
공식 코드Long Story Short: a Summarize-then-Search Method for Long Video Question Answering
https://github.com/JonnyS1226/Ego4d_mq_3rd_solution
공식 코드ReLER@ZJU Submission to the Ego4D Moment Queries Challenge 2022
https://github.com/JoshuaEbenezer/ChipQA
공식 코드ChipQA: No-Reference Video Quality Prediction via Space-Time Chips
https://github.com/JoshuaEbenezer/ChipQA-0
공식 코드No-Reference Video Quality Assessment Using Space-Time Chips
https://github.com/JunHeum/BMBC
공식 코드BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation
https://github.com/JunaidCS032/MOTBeyondPixels
공식 코드Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking
https://github.com/JunweiLiang/Multiverse
공식 코드The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction
https://github.com/JunweiLiang/VERA_Shooter_Localization
공식 코드https://github.com/JustinYuu/MACIL_SD
공식 코드https://github.com/JustinYuu/MM_Pyramid
공식 코드https://github.com/Jyxarthur/flowsam
공식 코드https://github.com/KAIST-VICLab/SkateFormer
공식 코드SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition
https://github.com/KAIST-VICLab/TDSM
공식 코드TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action Recognition
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