Papers with Code 아카이브
Video Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 16
- 보존된 평가 기록
- 130
<span style="color:grey; opacity: 0.6">( Various Video Generation Tasks. Gif credit: MaGViT )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AlexiaJM/Deep-learning-with-cats
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/Bonennult/video_prediction
https://github.com/CNC-IISER-BHOPAL/Any-Body-Can-Dance
https://github.com/ChenDelong1999/VirtualConductor
VirtualConductor: Music-driven Conducting Video Generation System
https://github.com/DLHacks/mocogan
MoCoGAN: Decomposing Motion and Content for Video Generation
https://github.com/DepthAnything/Video-Depth-Anything
Video Depth Anything: Consistent Depth Estimation for Super-Long Videos
https://github.com/DevashishJoshi/Transferring-GANs-FYP
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/DwightFoster/Pytorch-TecoGan
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/EnVision-Research/MotionInversion
https://github.com/Fuhui-Chen/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/G-U-N/Awesome-Consistency-Models
https://github.com/G-U-N/Stable-Consistency-Tuning
https://github.com/GitHubXlong/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/GongyeLiu/StyleCrafter-SDXL
StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter
https://github.com/HERMINDERSINGH1234/ML_Extra_Resolution_Increases
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/HappyBahman/ldvdGAN
MoCoGAN: Decomposing Motion and Content for Video Generation
https://github.com/Harrypotterrrr/DVD-GAN
https://github.com/Hritikbansal/videophy/tree/main/VIDEOPHY2
VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video Generation
https://github.com/IEIT-AGI/DropletVideo
DropletVideo: A Dataset and Approach to Explore Integral Spatio-Temporal Consistent Video Generation
https://github.com/IEWbgfnYDwHRoRRSKtkdyMDUzgdwuBYgDKtDJWd/doldy
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/Jaso1024/Refining-Generated-Videos
https://github.com/JonathanLehner/TecoGan-downloader
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/KbeautyHair/BaselineModel
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/Kinyugo/consistency_models
https://github.com/LAION-AI/phenaki
Phenaki: Variable Length Video Generation From Open Domain Textual Description
https://github.com/Lotayou/everybody_dance_now_pytorch
https://github.com/MIT-Omnipush/video-prediction
https://github.com/MLX15/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/MS-P3/code7/tree/main/van
VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs
https://github.com/MicroprocessorX069/Comparison-of-DC-GANS-and-SA-GANS
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/MindCode-4/code-12/tree/main/GAN-master
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/MindCode-4/code-7/tree/main/GAN-master
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/MindSpore-paper-code-3/code2/tree/main/dcgan
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/MindSpore-scientific-2/code-5/tree/main/INR-Implicit-Neural-Representations-with-Periodic-Activation-Functions
INR-V: A Continuous Representation Space for Video-based Generative Tasks
https://github.com/MindSpore-scientific/code-10/tree/main/PR_Product
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.