Papers with Code 아카이브
Unconditional Video Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Rethinking Video Super-Resolution: Towards Diffusion-Based Methods without Motion Alignment
FaceVid-1K: A Large-Scale High-Quality Multiracial Human Face Video Dataset
StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN
0-1 laws for pattern occurrences in phylogenetic trees and networks
StyleInV: A Temporal Style Modulated Inversion Network for Unconditional Video Generation
DDLP: Unsupervised Object-Centric Video Prediction with Deep Dynamic Latent Particles
Video Diffusion Models with Local-Global Context Guidance
MOSO: Decomposing MOtion, Scene and Object for Video Prediction
MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs
CelebV-HQ: A Large-Scale Video Facial Attributes Dataset
Video Diffusion Models
Continuous-Time Video Generation via Learning Motion Dynamics with Neural ODE
Disentangled Recurrent Wasserstein Autoencoder
Latent Neural Differential Equations for Video Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.