https://github.com/piotrkawa/audio-deepfake-adversarial-attacks
공식 코드Defense Against Adversarial Attacks on Audio DeepFake Detection
Papers with Code 아카이브
3D Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
- 10
- 보존된 평가 기록
- 44
3D Reconstruction is the task of creating a 3D model or representation of an object or scene from 2D images or other data sources. The goal of 3D reconstruction is to create a virtual representation of an object or scene that can be used for a variety of purposes, such as visualization, animation, simulation, and analysis. It can be used in fields such as computer vision, robotics, and virtual reality.
Image: Gwak et al
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/piotrkawa/deepfake-whisper-features
공식 코드https://github.com/piotrkawa/specrnet
공식 코드SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection
https://github.com/pkqbajng/cgformer
공식 코드Context and Geometry Aware Voxel Transformer for Semantic Scene Completion
https://github.com/pku-vcl-3dv/slam3r
공식 코드SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videos
https://github.com/pmh47/dirt
공식 코드Learning single-image 3D reconstruction by generative modelling of shape, pose and shading
https://github.com/polimi-ispl/icpr2020dfdc
공식 코드https://github.com/postech-ami/fastmetro
공식 코드Cross-Attention of Disentangled Modalities for 3D Human Mesh Recovery with Transformers
https://github.com/prajwalsingh/TreeGCN-ED
공식 코드TreeGCN-ED: Encoding Point Cloud using a Tree-Structured Graph Network
https://github.com/pramishp/ihuman
공식 코드iHuman: Instant Animatable Digital Humans From Monocular Videos
https://github.com/pratikpv/mri_gan_deepfake
공식 코드MRI-GAN: A Generalized Approach to Detect DeepFakes using Perceptual Image Assessment
https://github.com/prbonn/shine_mapping
공식 코드SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations
https://github.com/preddy5/g3dr
공식 코드https://github.com/princeton-vl/infinigen
공식 코드https://github.com/prs-eth/ResDepth
공식 코드ResDepth: A Deep Residual Prior For 3D Reconstruction From High-resolution Satellite Images
https://github.com/prstrive/unimvsnet
공식 코드Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation
https://github.com/pterhoer/DeepFakeAnnotations
공식 코드Analyzing Fairness in Deepfake Detection With Massively Annotated Databases
https://github.com/pubgeo/core3d-open
공식 코드https://github.com/purdue-m2/auc_fairness_with_noisy_groups
공식 코드Preserving AUC Fairness in Learning with Noisy Protected Groups
https://github.com/purdue-m2/fairness-generalization
공식 코드https://github.com/purdue-m2/sp_cup
공식 코드https://github.com/qianyiwu/objectsdf_plus
공식 코드ObjectSDF++: Improved Object-Compositional Neural Implicit Surfaces
https://github.com/qianyiwu/objsdf
공식 코드https://github.com/qihanggh/imrc
공식 코드Evaluate Geometry of Radiance Fields with Low-frequency Color Prior
https://github.com/qingqingle/mesc-3d
공식 코드MESC-3D:Mining Effective Semantic Cues for 3D Reconstruction from a Single Image
https://github.com/qingyuliu/exposing-the-deception
공식 코드Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection
https://github.com/qiqi-dai/3dinvnet
공식 코드3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion
https://github.com/qiqitao77/awesome-comprehensive-deepfake-detection
공식 코드Evolving from Single-modal to Multi-modal Facial Deepfake Detection: A Survey
https://github.com/qiqitao77/comprehensive-advances-in-deepfake-detection-spanning-diverse-modalities
공식 코드Evolving from Single-modal to Multi-modal Facial Deepfake Detection: A Survey
https://github.com/qt-zhu/aa-rmvsnet
공식 코드AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network
https://github.com/quanjinsheng/particlegs
공식 코드ParticleGS: Particle-Based Dynamics Modeling of 3D Gaussians for Prior-free Motion Extrapolation
https://github.com/quarxilon/generator_attribution
공식 코드Transferable Class-Modelling for Decentralized Source Attribution of GAN-Generated Images
https://github.com/quyans/deocc123
공식 코드DeOcc-1-to-3: 3D De-Occlusion from a Single Image via Self-Supervised Multi-View Diffusion
https://github.com/radekd91/emoca
공식 코드https://github.com/raining-dev/avt2-dwf
공식 코드AVT2-DWF: Improving Deepfake Detection with Audio-Visual Fusion and Dynamic Weighting Strategies
https://github.com/rainy-xu/tall4deepfake
공식 코드Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.