Papers with Code 아카이브
3D Face Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 9
- 보존된 평가 기록
- 94
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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hangyu94/ada-cm
공식 코드Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin
https://github.com/hanssuny/diffam
공식 코드DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection
https://github.com/hanweikung/face_anon_simple
공식 코드https://github.com/hanwu3125/os-sketch
공식 코드One-shot Face Sketch Synthesis in the Wild via Generative Diffusion Prior and Instruction Tuning
https://github.com/haofanwang/accurate-head-pose
공식 코드https://github.com/haoosz/ffr-net
공식 코드A Unified Framework for Masked and Mask-Free Face Recognition via Feature Rectification
https://github.com/haoxin917/3dface
공식 코드3D Face Reconstruction Using A Spectral-Based Graph Convolution Encoder
https://github.com/happy-jihye/Cartoon-StyleGan2
공식 코드https://github.com/happynear/AMSoftmax
공식 코드https://github.com/happynear/NormFace
공식 코드https://github.com/happynear/PainRegression
공식 코드Regularizing Face Verification Nets For Pain Intensity Regression
https://github.com/hardik-uppal/teacher-student-gan
공식 코드Teacher-Student Adversarial Depth Hallucination to Improve Face Recognition
https://github.com/harlanhong/actalker
공식 코드https://github.com/harlanhong/cvpr2022-dagan
공식 코드Depth-Aware Generative Adversarial Network for Talking Head Video Generation
https://github.com/harlanhong/iccv2023-mcnet
공식 코드https://github.com/hasanhuz/SpanEmo
공식 코드SpanEmo: Casting Multi-label Emotion Classification as Span-prediction
https://github.com/hasanirtiza/Pedestron
공식 코드Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection
https://github.com/hashtag00002/tt-df
공식 코드TT-DF: A Large-Scale Diffusion-Based Dataset and Benchmark for Human Body Forgery Detection
https://github.com/heathcliff-saku/dopatch
공식 코드Distributional Modeling for Location-Aware Adversarial Patches
https://github.com/hechmik/voxceleb_enrichment_age_gender
공식 코드https://github.com/helme/ecg_ptbxl_benchmarking
공식 코드Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
https://github.com/hengruilou/deepfacegen
공식 코드A Large-scale Universal Evaluation Benchmark For Face Forgery Detection
https://github.com/hengxyz/Dynamic_multi-task-learning
공식 코드Dynamic Multi-Task Learning for Face Recognition with Facial Expression
https://github.com/hengxyz/cari-visual-recognition-via-multitask-learning
공식 코드Dynamic Deep Multi-task Learning for Caricature-Visual Face Recognition
https://github.com/heqianpei/diga
공식 코드Distributionally Generative Augmentation for Fair Facial Attribute Classification
https://github.com/hertasecurity/LTFT
공식 코드Long-Term Face Tracking for Crowded Video-Surveillance Scenarios
https://github.com/highwaywu/st-ddl
공식 코드Exploring Spatial-Temporal Features for Deepfake Detection and Localization
https://github.com/hjy1312/AGPM
공식 코드Attention-guided Progressive Mapping for Profile Face Recognition
https://github.com/hm-choi/blind-match
공식 코드https://github.com/honghanh2104/t2a-think-twice-before-adaptation
공식 코드https://github.com/hongshuochen/DefakeHop
공식 코드DefakeHop: A Light-Weight High-Performance Deepfake Detector
https://github.com/hotfinda/Compensation-sampling
공식 코드Compensation Sampling for Improved Convergence in Diffusion Models
https://github.com/huage001/dynast
공식 코드DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation
https://github.com/huangjiadidi/DeepFakeMnist
공식 코드https://github.com/huangyangyu/ADNet
공식 코드ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.