https://github.com/metavisionlab/metafbp
공식 코드MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction
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.
MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction
Single camera pose estimation using Bayesian filtering and Kinect motion priors
Multi-spectral Class Center Network for Face Manipulation Detection and Localization
DigiFace-1M: 1 Million Digital Face Images for Face Recognition
Robust and Low-Rank Representation for Fast Face Identification with Occlusions
ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
Uncertainty-aware Label Distribution Learning for Facial Expression Recognition
Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
VisioPhysioENet: Multimodal Engagement Detection using Visual and Physiological Signals
DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection
Transformer Network for Semantically-Aware and Speech-Driven Upper-Face Generation
LoRAX: LoRA eXpandable Networks for Continual Synthetic Image Attribution
GOTCHA: Real-Time Video Deepfake Detection via Challenge-Response
Cluster and Aggregate: Face Recognition with Large Probe Set
DCFace: Synthetic Face Generation with Dual Condition Diffusion Model
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild
The Fairness Stitch: Unveiling the Potential of Model Stitching in Neural Network De-Biasing
TopoFR: A Closer Look at Topology Alignment on Face Recognition
Improved Xception with Dual Attention Mechanism and Feature Fusion for Face Forgery Detection
Real-time Facial Expression Recognition "In The Wild'' by Disentangling 3D Expression from Identity
ARoFace: Alignment Robustness to Improve Low-Quality Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.