https://github.com/pixelite1201/agora_evaluation
공식 코드AGORA: Avatars in Geography Optimized for Regression Analysis
Papers with Code 아카이브
3D Human Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 9
- 평가표
- 25
- 보존된 평가 기록
- 447
3D Human Pose Estimation is a computer vision task that involves estimating the 3D positions and orientations of body joints and bones from 2D images or videos. The goal is to reconstruct the 3D pose of a person in real-time, which can be used in a variety of applications, such as virtual reality, human-computer interaction, and motion analysis.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pku-yuangroup/envision3d
공식 코드https://github.com/pkunliu/isotropic3d
공식 코드Isotropic3D: Image-to-3D Generation Based on a Single CLIP Embedding
https://github.com/poker-xu/ssr-stf
공식 코드Optimizing Local-Global Dependencies for Accurate 3D Human Pose Estimation
https://github.com/postech-ami/fastmetro
공식 코드Cross-Attention of Disentangled Modalities for 3D Human Mesh Recovery with Transformers
https://github.com/prasunroy/mcma
공식 코드Exploring Mutual Cross-Modal Attention for Context-Aware Human Affordance Generation
https://github.com/prosperolo/gst
공식 코드GST: Precise 3D Human Body from a Single Image with Gaussian Splatting Transformers
https://github.com/proteus1991/RawVSR
공식 코드Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference
https://github.com/psyai-net/EmoTalk_release
공식 코드EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation
https://github.com/psyai-net/SelfTalk_release
공식 코드SelfTalk: A Self-Supervised Commutative Training Diagram to Comprehend 3D Talking Faces
https://github.com/qingshi9974/BMVC2021-Hierarchical-Graph-Networks-for-3D-Human-Pose-Estimation
공식 코드https://github.com/qitaozhao/poseformerv2
공식 코드PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation
https://github.com/rassaire/DmoGpm
공식 코드https://github.com/redrock303/revisiting-temporal-alignment-for-video-restoration
공식 코드https://github.com/rese1f/PoseDA
공식 코드https://github.com/researchmm/TTVSR
공식 코드Learning Trajectory-Aware Transformer for Video Super-Resolution
https://github.com/researchmm/ftvsr
공식 코드Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution
https://github.com/researchmm/vqd-sr
공식 코드Learning Data-Driven Vector-Quantized Degradation Model for Animation Video Super-Resolution
https://github.com/robot-perception-group/airpose
공식 코드AirPose: Multi-View Fusion Network for Aerial 3D Human Pose and Shape Estimation
https://github.com/rozumden/DeFMO
공식 코드https://github.com/rozumden/deblatting_python
공식 코드https://github.com/rozumden/fmo-deblurring-benchmark
공식 코드https://github.com/rtous/lester
공식 코드Lester: rotoscope animation through video object segmentation and tracking
https://github.com/ruihangao/tactiledreamfusion
공식 코드Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation
https://github.com/ryanxingql/winner-ntire22-vqe
공식 코드Progressive Training of A Two-Stage Framework for Video Restoration
https://github.com/saidwivedi/DSR
공식 코드Learning to Regress Bodies from Images using Differentiable Semantic Rendering
https://github.com/saidwivedi/TokenHMR
공식 코드TokenHMR: Advancing Human Mesh Recovery with a Tokenized Pose Representation
https://github.com/sail-sg/mvp
공식 코드https://github.com/seathiefwang/RankHeadPose
공식 코드RankPose: Learning Generalised Feature with Rank Supervision for Head Pose Estimation
https://github.com/seblutz/JointFormer
공식 코드https://github.com/senakicir/ActiveMoCap
공식 코드ActiveMoCap: Optimized Viewpoint Selection for Active Human Motion Capture
https://github.com/seonghwanseo/molvoxel
공식 코드PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
https://github.com/seonghwanseo/pharmaconet
공식 코드PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
https://github.com/shahjahan0275/Flex-GCN
공식 코드Flexible graph convolutional network for 3D human pose estimation
https://github.com/shangwei5/st-avsr
공식 코드Arbitrary-Scale Video Super-Resolution with Structural and Textural Priors
https://github.com/shaohua-pan/RobustCap
공식 코드Fusing Monocular Images and Sparse IMU Signals for Real-time Human Motion Capture
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.