Papers with Code 아카이브
3D Face Reconstruction
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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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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RetinaFace: Single-stage Dense Face Localisation in the Wild
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Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
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RetinaFace: Single-stage Dense Face Localisation in the Wild
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https://github.com/ibendrup/MulticolumnNetwork
https://github.com/icey-zhang/MTCNN_paddle
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/icey-zhang/RetinaFace_paddle
RetinaFace: Single-stage Dense Face Localisation in the Wild
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ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
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TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
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FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/ililily/retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/interestingzhuo/foodretreival
https://github.com/irynakostyshyn/A-Style-Based-Generator-Architecture-for-GAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
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Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models
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