Papers with Code 아카이브
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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kuhnkeF/ABAW2020TNT
공식 코드https://github.com/kunkunlin1221/InstructFLIP
공식 코드InstructFLIP: Exploring Unified Vision-Language Model for Face Anti-spoofing
https://github.com/kwanyun/FFaceNeRF
공식 코드https://github.com/kwanyun/LeGO_code
공식 코드https://github.com/kwanyun/StyleSketch
공식 코드Stylized Face Sketch Extraction via Generative Prior with Limited Data
https://github.com/l8cv/busterx
공식 코드BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation
https://github.com/laiyingxin2/dadf
공식 코드Detect Any Deepfakes: Segment Anything Meets Face Forgery Detection and Localization
https://github.com/laiyingxin2/gm-df
공식 코드https://github.com/laiyingxin2/shield
공식 코드https://github.com/lalithbharadwajbaru/wavelet-clip
공식 코드Wavelet-Driven Generalizable Framework for Deepfake Face Forgery Detection
https://github.com/langlrsw/AAL-unbiased-authentication
공식 코드https://github.com/lattas/avatarme
공식 코드AvatarMe: Realistically Renderable 3D Facial Reconstruction "in-the-wild"
https://github.com/leaffeall/DFS-GDD
공식 코드Decoupling Forgery Semantics for Generalizable Deepfake Detection
https://github.com/learninginvision/spu
공식 코드Self-Paced Deep Regression Forests with Consideration of Ranking Fairness
https://github.com/leebebeto/inter-prototype
공식 코드Improving Face Recognition with Large Age Gaps by Learning to Distinguish Children
https://github.com/leeisack/latent-ofer
공식 코드https://github.com/lehinevych/SAAN
공식 코드Self-attention aggregation network for video face representation and recognition
https://github.com/lelechen63/Talking-head-Generation-with-Rhythmic-Head-Motion
공식 코드https://github.com/lelechen63/talking-head-generation-survey
공식 코드What comprises a good talking-head video generation?: A Survey and Benchmark
https://github.com/leminhbinh0209/add
공식 코드https://github.com/leminhbinh0209/cvpr24-fas
공식 코드https://github.com/lemuelpuglisi/synthba
공식 코드SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions
https://github.com/leo7r/swiftface
공식 코드https://github.com/leoxiaobin/deep-high-resolution-net.pytorch
공식 코드High-Resolution Representations for Labeling Pixels and Regions
https://github.com/lexieyang/face_img_inpainting
공식 코드https://github.com/lhyfst/dsfnet
공식 코드DSFNet: Dual Space Fusion Network for Occlusion-Robust 3D Dense Face Alignment
https://github.com/li-8023/multi-function-face-recognition
공식 코드https://github.com/li-wanhua/label2label
공식 코드Label2Label: A Language Modeling Framework for Multi-Attribute Learning
https://github.com/liangchen527/sladd
공식 코드Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
https://github.com/lidan1/PhotoSketchMAN
공식 코드High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks
https://github.com/ligaripash/KWtQ-face-alignment
공식 코드https://github.com/ligaripash/facecoresetnet
공식 코드FaceCoresetNet: Differentiable Coresets for Face Set Recognition
https://github.com/lightdxy/ict_deepfake
공식 코드Protecting Celebrities from DeepFake with Identity Consistency Transformer
https://github.com/lingxiao-he/Partial-Person-ReID
공식 코드https://github.com/liqi-casia/faceswapper
공식 코드Learning Disentangled Representation for One-shot Progressive Face Swapping
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.