https://github.com/albertpumarola/GANimation
공식 코드GANimation: Anatomically-aware Facial Animation from a Single Image
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.
GANimation: Anatomically-aware Facial Animation from a Single Image
DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic Models
It's All in the Head: Representation Knowledge Distillation through Classifier Sharing
Learning from Failure: Training Debiased Classifier from Biased Classifier
ASMNet: a Lightweight Deep Neural Network for Face Alignment and Pose Estimation
ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment
GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face Synthesis
Facial Landmark Points Detection Using Knowledge Distillation-Based Neural Networks
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition
Faithful Target Attribute Prediction in Neural Machine Translation
Robustness Analysis of Deep Learning Frameworks on Mobile Platforms
FastFace: Fast-converging Scheduler for Large-scale Face Recognition Training with One GPU
UnMarker: A Universal Attack on Defensive Image Watermarking
Shape Preserving Facial Landmarks with Graph Attention Networks
Unsupervised Single-shot Depth Estimation using Perceptual Reconstruction
Fast and Interpretable Face Identification for Out-Of-Distribution Data Using Vision Transformers
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing
Extending Information Bottleneck Attribution to Video Sequences
SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection
Laughing Matters: Introducing Laughing-Face Generation using Diffusion Models
A Dataless FaceSwap Detection Approach Using Synthetic Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.