https://github.com/learninginvision/spu
공식 코드Self-Paced Deep Regression Forests with Consideration of Ranking Fairness
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.
https://github.com/leebebeto/inter-prototype
공식 코드Improving Face Recognition with Large Age Gaps by Learning to Distinguish Children
https://github.com/leeisack/latent-ofer
공식 코드https://github.com/lehinevych/SAAN
공식 코드Self-attention aggregation network for video face representation and recognition
https://github.com/lelechen63/Talking-head-Generation-with-Rhythmic-Head-Motion
공식 코드https://github.com/lelechen63/talking-head-generation-survey
공식 코드What comprises a good talking-head video generation?: A Survey and Benchmark
https://github.com/leminhbinh0209/add
공식 코드https://github.com/lemuelpuglisi/synthba
공식 코드SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions
https://github.com/leo7r/swiftface
공식 코드https://github.com/leoxiaobin/deep-high-resolution-net.pytorch
공식 코드High-Resolution Representations for Labeling Pixels and Regions
https://github.com/lexieyang/face_img_inpainting
공식 코드https://github.com/lhyfst/dsfnet
공식 코드DSFNet: Dual Space Fusion Network for Occlusion-Robust 3D Dense Face Alignment
https://github.com/li-8023/multi-function-face-recognition
공식 코드https://github.com/li-wanhua/label2label
공식 코드Label2Label: A Language Modeling Framework for Multi-Attribute Learning
https://github.com/liangchen527/sladd
공식 코드Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
https://github.com/lidan1/PhotoSketchMAN
공식 코드High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks
https://github.com/ligaripash/KWtQ-face-alignment
공식 코드https://github.com/ligaripash/facecoresetnet
공식 코드FaceCoresetNet: Differentiable Coresets for Face Set Recognition
https://github.com/lightdxy/ict_deepfake
공식 코드Protecting Celebrities from DeepFake with Identity Consistency Transformer
https://github.com/lingxiao-he/Partial-Person-ReID
공식 코드https://github.com/liqi-casia/faceswapper
공식 코드Learning Disentangled Representation for One-shot Progressive Face Swapping
https://github.com/lisha-chen/Deep-structured-facial-landmark-detection
공식 코드https://github.com/lissomx/MSP
공식 코드Latent Space Factorisation and Manipulation via Matrix Subspace Projection
https://github.com/littlejuyan/df_fairness
공식 코드https://github.com/liu-tianchi/nes2net
공식 코드Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofing
https://github.com/liuf1990/3DFC
공식 코드https://github.com/liuhw01/Norface
공식 코드Norface: Improving Facial Expression Analysis by Identity Normalization
https://github.com/liutaocode/DiffDub
공식 코드DiffDub: Person-generic Visual Dubbing Using Inpainting Renderer with Diffusion Auto-encoder
https://github.com/liuwei16/CSP
공식 코드Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection
https://github.com/liuyuisanai/trojans-face-recognizer
공식 코드https://github.com/liuyvchi/NROLL
공식 코드Boosting Semi-Supervised Face Recognition with Noise Robustness
https://github.com/liuyvchi/mie-x
공식 코드How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset?
https://github.com/liuzhengzhe/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild
공식 코드Global Texture Enhancement for Fake Face Detection in the Wild
https://github.com/liwenchao0615/zerrinnet
공식 코드Zero-Shot Enhancement of Low-Light Image Based on Retinex Decomposition
https://github.com/lizhaoliu-Lec/DAS
공식 코드https://github.com/lizhenwangt/faceverse
공식 코드FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.