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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.
HonestFace: Towards Honest Face Restoration with One-Step Diffusion Model
Preserving AUC Fairness in Learning with Noisy Protected Groups
HyperFake: Hyperspectral Reconstruction and Attention-Guided Analysis for Advanced Deepfake Detection
Think Twice before Adaptation: Improving Adaptability of DeepFake Detection via Online Test-Time Adaptation
What You Read Isn't What You Hear: Linguistic Sensitivity in Deepfake Speech Detection
3D Face Reconstruction Error Decomposed: A Modular Benchmark for Fair and Fast Method Evaluation
CAMME: Adaptive Deepfake Image Detection with Multi-Modal Cross-Attention
DualTalk: Dual-Speaker Interaction for 3D Talking Head Conversations
TokBench: Evaluating Your Visual Tokenizer before Visual Generation
Temporal and Spatial Feature Fusion Framework for Dynamic Micro Expression Recognition
Pose-invariant face recognition via feature-space pose frontalization
Beyond Face Swapping: A Diffusion-Based Digital Human Benchmark for Multimodal Deepfake Detection
CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation
DiffProb: Data Pruning for Face Recognition
FaceCrafter: Identity-Conditional Diffusion with Disentangled Control over Facial Pose, Expression, and Emotion
My Face Is Mine, Not Yours: Facial Protection Against Diffusion Model Face Swapping
Pura: An Efficient Privacy-Preserving Solution for Face Recognition
MIKU-PAL: An Automated and Standardized Multi-Modal Method for Speech Paralinguistic and Affect Labeling
Forensic deepfake audio detection using segmental speech features
Naturalness-Aware Curriculum Learning with Dynamic Temperature for Speech Deepfake Detection
Listen, Analyze, and Adapt to Learn New Attacks: An Exemplar-Free Class Incremental Learning Method for Audio Deepfake Source Tracing
Replay Attacks Against Audio Deepfake Detection
Colors Matter: AI-Driven Exploration of Human Feature Colors
BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation
Multiscale Adaptive Conflict-Balancing Model For Multimedia Deepfake Detection
Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation
Facial Recognition Leveraging Generative Adversarial Networks
BanglaFake: Constructing and Evaluating a Specialized Bengali Deepfake Audio Dataset
ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization
$\mathcal{A}LLM4ADD$: Unlocking the Capabilities of Audio Large Language Models for Audio Deepfake Detection
MAVOS-DD: Multilingual Audio-Video Open-Set Deepfake Detection Benchmark
Deepfake Forensic Analysis: Source Dataset Attribution and Legal Implications of Synthetic Media Manipulation
X-Edit: Detecting and Localizing Edits in Images Altered by Text-Guided Diffusion Models
TKFNet: Learning Texture Key Factor Driven Feature for Facial Expression Recognition
StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story Generation
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