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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>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DiffClean: Diffusion-based Makeup Removal for Accurate Age Estimation
InstructFLIP: Exploring Unified Vision-Language Model for Face Anti-spoofing
Non-Adaptive Adversarial Face Generation
MGFFD-VLM: Multi-Granularity Prompt Learning for Face Forgery Detection with VLM
Attributes Shape the Embedding Space of Face Recognition Models
Data-Driven Meta-Analysis and Public-Dataset Evaluation for Sensor-Based Gait Age Estimation
Multi-Modal Face Anti-Spoofing via Cross-Modal Feature Transitions
MEDTalk: Multimodal Controlled 3D Facial Animation with Dynamic Emotions by Disentangled Embedding
CorrDetail: Visual Detail Enhanced Self-Correction for Face Forgery Detection
Beyond Spatial Frequency: Pixel-wise Temporal Frequency-based Deepfake Video Detection
DDL: A Dataset for Interpretable Deepfake Detection and Localization in Real-World Scenarios
Multimodal Prompt Alignment for Facial Expression Recognition
Post-training for Deepfake Speech Detection
On the Burstiness of Faces in Set
Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images
Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks
Identifying Physically Realizable Triggers for Backdoored Face Recognition Networks
IndieFake Dataset: A Benchmark Dataset for Audio Deepfake Detection
Advancing Talking Head Generation: A Comprehensive Survey of Multi-Modal Methodologies, Datasets, Evaluation Metrics, and Loss Functions
SELFI: Selective Fusion of Identity for Generalizable Deepfake Detection
AGE-US: automated gestational age estimation based on fetal ultrasound images
Spotting tell-tale visual artifacts in face swapping videos: strengths and pitfalls of CNN detectors
One-shot Face Sketch Synthesis in the Wild via Generative Diffusion Prior and Instruction Tuning
MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution
Foundation Artificial Intelligence Models for Health Recognition Using Face Photographs (FAHR-Face)
Overcoming Occlusions in the Wild: A Multi-Task Age Head Approach to Age Estimation
Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-Processing
A Watermark for Auto-Regressive Image Generation Models
FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes
From Sharpness to Better Generalization for Speech Deepfake Detection
FaceLiVT: Face Recognition using Linear Vision Transformer with Structural Reparameterization For Mobile Device
LLMs Are Not Yet Ready for Deepfake Image Detection
Balancing Tails when Comparing Distributions: Comprehensive Equity Index (CEI) with Application to Bias Evaluation in Operational Face Biometrics
Enhancing Deepfake Detection using SE Block Attention with CNN
Underage Detection through a Multi-Task and MultiAge Approach for Screening Minors in Unconstrained Imagery
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.