Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

2

보존된 평가 기록

3

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Olksndr/FaceShifter

FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping

https://github.com/OmarEl-Khashab/FerNeXt-Facial-Expression-Recognition-Using-ConvNeXt-with-Channel-Attention

https://github.com/One-sixth/CVAE-GAN_tensorlayer

CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training

https://github.com/Ontheway361/dul-pytorch

Data Uncertainty Learning in Face Recognition

https://github.com/Ontheway361/pfld-pytorch

PFLD: A Practical Facial Landmark Detector

https://github.com/Oorgien/Scene-Inpainting

Image Fine-grained Inpainting

https://github.com/OpenSpaceAI/TI-Face

TIFace: Improving Facial Reconstruction through Tensorial Radiance Fields and Implicit Surfaces

https://github.com/OpenTalker/SadTalker

SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation

https://github.com/Ostyk/walk-bot

Proximal Policy Optimization Algorithms

https://github.com/PKU-EPIC/PartManip

What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study

https://github.com/PPPrior/i3d-pytorch

Deep Residual Learning for Image Recognition

https://github.com/PaParaZz1/PointNet

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter07

Progressive Growing of GANs for Improved Quality, Stability, and Variation

https://github.com/PaddlePaddle/PLSC

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/PaddlePaddle/PaddleDetection

Deep Residual Learning for Image Recognition

https://github.com/PaddlePaddle/PaddleSeg

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/Panagiotou/Procedural3DTerrain

Progressive Growing of GANs for Improved Quality, Stability, and Variation

https://github.com/Panagiotou/Random3DTerrain

Progressive Growing of GANs for Improved Quality, Stability, and Variation

https://github.com/PaulCharnay/Projet_AIF

Sample Efficient Actor-Critic with Experience Replay

https://github.com/Pechckin/MountainCar

Continuous control with deep reinforcement learning

https://github.com/PeterJochem/Deep_RL

Continuous control with deep reinforcement learning

https://github.com/PeterJochem/Double_Deep_QLearning

Continuous control with deep reinforcement learning

https://github.com/Philori22/DDPG-aigym

Continuous control with deep reinforcement learning

https://github.com/Phimos/Paddle-PointNet

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/PietroMelzi/GANDiffFace

Synthetic Data for the Mitigation of Demographic Biases in Face Recognition

https://github.com/Podidiving/lgsc-for-fas-pytorch

Learning Generalized Spoof Cues for Face Anti-spoofing

https://github.com/PolarisAlpha/darknet

Focal Loss for Dense Object Detection

https://github.com/PolyArch/fp-diannao

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Popoooo/darknet_test

Focal Loss for Dense Object Detection

https://github.com/Porechniy13/FaceReco

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/PowerOfDream/digitx

Network In Network

https://github.com/PrasannaPulakurthi/ProGAN_PyTorch

Progressive Growing of GANs for Improved Quality, Stability, and Variation

https://github.com/PrashantRanjan09/Structured-Self-Attentive-Sentence-Embedding

A Structured Self-attentive Sentence Embedding

https://github.com/PrashanthaTP/wav2mov

A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild

https://github.com/Prateek93a/DoodleAI

Network In Network

https://github.com/PuchatekwSzortach/face_detection

Multi-view Face Detection Using Deep Convolutional Neural Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.