https://github.com/ZoieMo/Multi-task
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
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Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
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ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
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ArcFace: Additive Angular Margin Loss for Deep Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.