https://github.com/opendrivelab/uniad
Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability
Papers with Code 아카이브
Video Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 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/p-hss/consistency-climate-downscaling
https://github.com/pfnet-research/tgan
Temporal Generative Adversarial Nets with Singular Value Clipping
https://github.com/phantom-video/phantom
Phantom: Subject-consistent video generation via cross-modal alignment
https://github.com/phixion/phixion
Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model
https://github.com/pixeli99/svd_xtend
TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion Models
https://github.com/pku-yuangroup/wf-vae
https://github.com/pmh47/o3v
Unsupervised object-centric video generation and decomposition in 3D
https://github.com/princeton-nlp/copycat
https://github.com/raahii/evan
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/raahii/video-gans-evaluation
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/rajatsahay/Pose2Pose
https://github.com/recluse27/Colorizator
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/reyllama/mixdl
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/robbergen/trgan
Temporal Generative Adversarial Nets with Singular Value Clipping
https://github.com/roboterax/video-prediction-policy
Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
https://github.com/rucmlcv/L2M-GAN
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/ryanxingql/image-quality-assessment-toolbox
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/sainzerjj/sferd
https://github.com/saiyan-world/goku
https://github.com/sdxone/resolution
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/sergkuzn148/lol3
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/sergkuzn148/stg
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/sharegpt4omni/sharegpt4video
ShareGPT4Video: Improving Video Understanding and Generation with Better Captions
https://github.com/shim0114/T2V-Diffusion-Search
Inference-Time Text-to-Video Alignment with Diffusion Latent Beam Search
https://github.com/sibozhang/Text2Video
https://github.com/sihyun-yu/digan
Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks
https://github.com/sihyun-yu/pvdm
Video Probabilistic Diffusion Models in Projected Latent Space
https://github.com/sjtuplayer/sara
SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation
https://github.com/snap-research/panda-70m
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers
https://github.com/sreerajr000/consistency-models
https://github.com/srpkdyy/VideoLDM
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
https://github.com/suraj-nair-1/google-research/tree/master/hierarchical_foresight
https://github.com/synce1234/FTGAN_custom
Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture
https://github.com/tencent-hunyuan/hunyuancustom
HunyuanCustom: A Multimodal-Driven Architecture for Customized Video Generation
https://github.com/tencent-hunyuan/hunyuanvideo
HunyuanVideo: A Systematic Framework For Large Video Generative Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.