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3D Face Animation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Image: Cudeiro et al
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks
FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal Loss
Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objects
Self-Enhanced Convolutional Network for Facial Video Hallucination
Frame and Feature-Context Video Super-Resolution
Deformable Non-local Network for Video Super-Resolution
Robust Online Video Super-Resolution Using an Efficient Alternating Projections Scheme
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models
Intra-frame Object Tracking by Deblatting
Capture, Learning, and Synthesis of 3D Speaking Styles
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
Fast Spatio-Temporal Residual Network for Video Super-Resolution
Recurrent Back-Projection Network for Video Super-Resolution
Two-Stream Action Recognition-Oriented Video Super-Resolution
3DSRnet: Video Super-resolution using 3D Convolutional Neural Networks
TDAN: Temporally Deformable Alignment Network for Video Super-Resolution
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
Learning for Video Super-Resolution through HR Optical Flow Estimation
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Perceptual Video Super Resolution with Enhanced Temporal Consistency
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution
Deep Back-Projection Networks For Super-Resolution
Frame-Recurrent Video Super-Resolution
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
Video Enhancement with Task-Oriented Flow
End-to-End Learning of Video Super-Resolution with Motion Compensation
A New Adaptive Video Super-Resolution Algorithm With Improved Robustness to Innovations
Detail-revealing Deep Video Super-resolution
Multiframe Motion Coupling for Video Super Resolution
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
XGBoost: A Scalable Tree Boosting System
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.