https://github.com/lucidrains/magvit2-pytorch
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
Papers with Code 아카이브
Video Generation
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- 2
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- 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/lucidrains/make-a-video-pytorch
https://github.com/lucidrains/nuwa-pytorch
NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
https://github.com/lucidrains/phenaki-pytorch
Phenaki: Variable Length Video Generation From Open Domain Textual Description
https://github.com/lucidrains/recurrent-interface-network-pytorch
https://github.com/lucidrains/video-diffusion-pytorch
https://github.com/martin220485/everybody_dance_now_pytorch
https://github.com/mattiasxu/Video-VQVAE
https://github.com/mayuelala/followyourcanvas
Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videos
https://github.com/mbinkowski/DeepSpeechDistances
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/megvii-research/conr
https://github.com/megvii-research/ijcai2023-conr
https://github.com/mehdidc/DALLE_clip_score
GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions
https://github.com/mil-tokyo/FTGAN
Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture
https://github.com/milenagazdieva/extremalneuraloptimaltransport
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/milmor/LDT
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/milmor/LadaGAN-pytorch
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/milmor/diffusion-transformer
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/milmor/diffusion-transformer-keras
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/milmor/ladagan
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/milmor/latent-diffusion-transformer
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/mindspore-lab/mindone
https://github.com/mindspore-lab/mindone/tree/master/examples/moviegen
https://github.com/mindspore-lab/mindone/tree/master/examples/svd
Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
https://github.com/minhnhat93/tf-SNDCGAN
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/mira-space/mira
MiraData: A Large-Scale Video Dataset with Long Durations and Structured Captions
https://github.com/mira-space/miradata
MiraData: A Large-Scale Video Dataset with Long Durations and Structured Captions
https://github.com/ml-gsai/concat-id
Concat-ID: Towards Universal Identity-Preserving Video Synthesis
https://github.com/mlakhal/gtnet
https://github.com/mseitzer/pytorch-fid
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/ngushchin/entropicotbenchmark
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/nvlabs/long-video-gan
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/obvious-research/OnlyFlow
OnlyFlow: Optical Flow based Motion Conditioning for Video Diffusion Models
https://github.com/onethousand1000/eg3d-projector
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/openai/consistency_models_cifar10
https://github.com/openai/consistencydecoder
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.