https://github.com/miraflow/DistilDIRE
공식 코드DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection
Papers with Code 아카이브
3D Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/mirevi/face-synthesizer-jvrb
공식 코드https://github.com/mit-ll/lorax_cil
공식 코드LoRAX: LoRA eXpandable Networks for Continual Synthetic Image Attribution
https://github.com/mittalgovind/GOTCHA-Deepfakes
공식 코드GOTCHA: Real-Time Video Deepfake Detection via Challenge-Response
https://github.com/mizhenxing/gbi-net
공식 코드Generalized Binary Search Network for Highly-Efficient Multi-View Stereo
https://github.com/mkhangg/refined_panoptic_mapping
공식 코드Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots
https://github.com/ml-gsai/microdreamer
공식 코드MicroDreamer: Efficient 3D Generation in $\sim$20 Seconds by Score-based Iterative Reconstruction
https://github.com/ml-struct-bio/cryobench
공식 코드CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM
https://github.com/mobarakol/dares
공식 코드https://github.com/momoailab/ultrapose
공식 코드UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model
https://github.com/monniert/unicorn
공식 코드Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency
https://github.com/monu-khicher-1/multi-stage-learning
공식 코드https://github.com/mosamdabhi/mbw
공식 코드https://github.com/mosamdabhi/mbw-data
공식 코드https://github.com/mrkoujan/DeepFaceFlow
공식 코드https://github.com/mrzzy2021/sculpturerecon
공식 코드https://github.com/msbrpp/MSBR
공식 코드https://github.com/muelea/buddi
공식 코드Generative Proxemics: A Prior for 3D Social Interaction from Images
https://github.com/mweiherer/matern-surface-reconstruction
공식 코드https://github.com/mzeegers/ADJUST
공식 코드ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography
https://github.com/n-nejatishahidin/pose_from_mid-level
공식 코드Object Pose Estimation using Mid-level Visual Representations
https://github.com/naruya/vaxnerf
공식 코드VaxNeRF: Revisiting the Classic for Voxel-Accelerated Neural Radiance Field
https://github.com/navamikairanda/phi_sft
공식 코드φ-SfT: Shape-from-Template with a Physics-Based Deformation Model
https://github.com/naver/dust3r
공식 코드https://github.com/naver/mast3r
공식 코드https://github.com/naver/must3r
공식 코드https://github.com/nctu-eva-lab/antifakeprompt
공식 코드AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors
https://github.com/nickwzk/streamsplat
공식 코드StreamSplat: Towards Online Dynamic 3D Reconstruction from Uncalibrated Video Streams
https://github.com/nicolaihaeni/corn
공식 코드Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision
https://github.com/nicolamarinello/offsetocc
공식 코드Camera-Only 3D Panoptic Scene Completion for Autonomous Driving through Differentiable Object Shapes
https://github.com/nihalsid/retrieval-fuse
공식 코드RetrievalFuse: Neural 3D Scene Reconstruction with a Database
https://github.com/nii-yamagishilab/ssl_vits_df
공식 코드Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative Analysis
https://github.com/nikolausdemmel/rootba
공식 코드https://github.com/nilslukas/gan-watermark
공식 코드PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators
https://github.com/ningyu1991/ArtificialGANFingerprints
공식 코드Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data
https://github.com/nirvanalan/ln3diff
공식 코드LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.