https://github.com/MindSpore-scientific/code-11/tree/main/INR-Implicit-Neural-Representations-with-Periodic-Activation-Functions
INR-V: A Continuous Representation Space for Video-based Generative Tasks
Papers with Code 아카이브
Video Generation
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<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/MindSpore-scientific/code-2/tree/main/stable-sam
Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
https://github.com/Mohanned-Elkholy/ResNet-GAN
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/Mosasaur5526/BCM-iCT-torch
https://github.com/NVlabs/eg3d
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/Novemser/deep-imitation
https://github.com/PKU-YuanGroup/MoE-LLaVA
https://github.com/PKU-YuanGroup/UniWorld-V1
https://github.com/RAGHAV2998/Everybody-Can-Dance-Now-Video-game-version-
https://github.com/RoboTurk-Platform/roboturk_real_dataset
https://github.com/STVIR/seg2vid
https://github.com/STomoya/ptfid
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/SUPERSHOPxyz/stylegan3-gradient
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/Sainzerjj/UniAnimate
UniAnimate: Taming Unified Video Diffusion Models for Consistent Human Image Animation
https://github.com/SakiRinn/mindspore-hp-vae-gan
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample
https://github.com/ShaoTengLiu/Video-P2P
https://github.com/Shenyi-Z/TaylorSeer
Accelerating Diffusion Transformers with Dual Feature Caching
https://github.com/Shi-qingyu/DeT
Decouple and Track: Benchmarking and Improving Video Diffusion Transformers for Motion Transfer
https://github.com/ShutoAraki/EverybodyDanceNow-Temporal-FaceGAN
https://github.com/ShwanMario/ASWD
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/SiskonEmilia/StyleGAN-PyTorch
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/Soldie/DeOldify-colorir-imagens-antigas
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/StanfordVL/roboturk_real_dataset
https://github.com/TIGER-AI-Lab/ImagenHub
GenAI Arena: An Open Evaluation Platform for Generative Models
https://github.com/Tianhao-Qi/DEADiff_code
StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter
https://github.com/TsinghuaC3I/Efficient-Diffusion-Models
Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices
https://github.com/UBC-Computer-Vision-Group/DwNet
MoCoGAN: Decomposing Motion and Content for Video Generation
https://github.com/VQAssessment/FAST-VQA-and-FasterVQA
https://github.com/Vchitect/Vchitect-2.0
Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models
https://github.com/VideoPRNUExtractor/Weighter
https://github.com/VincieD/TrafficSign_GAN_Benchmark
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/VisiumCH/AMLD2020-Dirty-Gancing
https://github.com/Wangt-CN/DisCo
MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion
https://github.com/Xiaohui9607/physical_interaction_video_prediction_pytorch
Unsupervised Learning for Physical Interaction through Video Prediction
https://github.com/Zasder3/Tganv2-PyTorch-Train-Sparsely--Generate-Densely
https://github.com/aayushbansal/Recycle-GAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.