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3D Video Frame Interpolation
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Single Trajectory Distillation for Accelerating Image and Video Style Transfer
StyleMaster: Stylize Your Video with Artistic Generation and Translation
UniVST: A Unified Framework for Training-free Localized Video Style Transfer
Noise Crystallization and Liquid Noise: Zero-shot Video Generation using Image Diffusion Models
Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model
Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images
LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model
WAIT: Feature Warping for Animation to Illustration video Translation using GANs
Universal Photorealistic Style Transfer: A Lightweight and Adaptive Approach
Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning
Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer
Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers
CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer
FateZero: Fusing Attentions for Zero-shot Text-based Video Editing
ColoristaNet for Photorealistic Video Style Transfer
VToonify: Controllable High-Resolution Portrait Video Style Transfer
CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer
Layered Neural Atlases for Consistent Video Editing
AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
Stylizing 3D Scene via Implicit Representation and HyperNetwork
Real-time Localized Photorealistic Video Style Transfer
Consistent Video Style Transfer via Relaxation and Regularization
Arbitrary Video Style Transfer via Multi-Channel Correlation
Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer
MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones
HyperCon: Image-To-Video Model Transfer for Video-To-Video Translation Tasks
Evolvement Constrained Adversarial Learning for Video Style Transfer
A Style-Aware Content Loss for Real-time HD Style Transfer
ReCoNet: Real-time Coherent Video Style Transfer Network
Characterizing and Improving Stability in Neural Style Transfer
Coherent Online Video Style Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.