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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/google-research/google-research/tree/master/implicit_pdf
공식 코드Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold
https://github.com/google-research/google-research/tree/master/keypose
공식 코드KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects
https://github.com/google-research/google-research/tree/master/metapose
공식 코드MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision
https://github.com/google-research/google-research/tree/master/special_orthogonalization
공식 코드https://github.com/google-research/maskgit
공식 코드https://github.com/google-research/maxim
공식 코드https://github.com/google-research/mint
공식 코드AI Choreographer: Music Conditioned 3D Dance Generation with AIST++
https://github.com/google-research/nerf-from-image
공식 코드Shape, Pose, and Appearance from a Single Image via Bootstrapped Radiance Field Inversion
https://github.com/google-research/pix2seq
공식 코드https://github.com/google-research/xmcgan_image_generation
공식 코드Cross-Modal Contrastive Learning for Text-to-Image Generation
https://github.com/google/imageinwords
공식 코드https://github.com/google/mediapipe
공식 코드https://github.com/google/prompt-to-prompt
공식 코드https://github.com/gorilla-lab-scut/bico-net
공식 코드BiCo-Net: Regress Globally, Match Locally for Robust 6D Pose Estimation
https://github.com/gorilla-lab-scut/dcl-net
공식 코드DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation
https://github.com/gorilla-lab-scut/mast
공식 코드Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose
https://github.com/gorilla-lab-scut/ss-conv
공식 코드https://github.com/gouqi666/rast
공식 코드Diversify Question Generation with Retrieval-Augmented Style Transfer
https://github.com/gpt4vision/OvSGTR
공식 코드https://github.com/graformer/graformer
공식 코드GraFormer: Graph Convolution Transformer for 3D Pose Estimation
https://github.com/great-whu/sf-loc
공식 코드SF-Loc: A Visual Mapping and Geo-Localization System based on Sparse Visual Structure Frames
https://github.com/greatenanoymous/rgbtrack
공식 코드RGBTrack: Fast, Robust Depth-Free 6D Pose Estimation and Tracking
https://github.com/greatlog/SWAHR-HumanPose
공식 코드Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation
https://github.com/greatx3/playable-game-generation
공식 코드https://github.com/grimalpaul/tiam
공식 코드TIAM -- A Metric for Evaluating Alignment in Text-to-Image Generation
https://github.com/grishavak/Patch_Ordering_as_a_Regularization_for_Inverse_Problems_in_Image_Processing
공식 코드Patch-Ordering as a Regularization for Inverse Problems in Image Processing
https://github.com/ground-a-video/ground-a-video
공식 코드Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models
https://github.com/gt-arc/apr-transformer
공식 코드https://github.com/gt-salt/positive-frames
공식 코드https://github.com/guangyliu/EISL
공식 코드Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
https://github.com/gudgud96/music-fader-nets
공식 코드https://github.com/gulucaptain/videoassembler
공식 코드MagDiff: Multi-Alignment Diffusion for High-Fidelity Video Generation and Editing
https://github.com/guobaoxiao/gct-net
공식 코드Graph Context Transformation Learning for Progressive Correspondence Pruning
https://github.com/guochengqian/magic123
공식 코드Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors
https://github.com/guoqiang-zhang-x/BDIA
공식 코드Exact Diffusion Inversion via Bi-directional Integration Approximation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.