https://github.com/shermanlian/swrn
공식 코드Sliding Window Recurrent Network for Efficient Video Super-Resolution
Papers with Code 아카이브
3D Human Pose Estimation
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- 평가표
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- 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/shijianjian/voxelkp
공식 코드VoxelKP: A Voxel-based Network Architecture for Human Keypoint Estimation in LiDAR Data
https://github.com/shirleymaxx/vmarker-pro
공식 코드https://github.com/shivangi-aneja/FaceTalk
공식 코드FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models
https://github.com/shubham-goel/4D-Humans
공식 코드Humans in 4D: Reconstructing and Tracking Humans with Transformers
https://github.com/sichun233746/motif
공식 코드https://github.com/sinzlab/cgnf
공식 코드Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions
https://github.com/sjtu-lucy/emoface
공식 코드https://github.com/sjtuxcx/ITES
공식 코드Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation
https://github.com/sjtuytc/betapose
공식 코드Estimating 6D Pose From Localizing Designated Surface Keypoints
https://github.com/smlc-nysbc/target-vae
공식 코드Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE
https://github.com/smplbody/hmr-benchmarks
공식 코드Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithms
https://github.com/soullessrobot/human-m3-dataset
공식 코드Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation in Outdoor Scenes
https://github.com/soullessrobot/lidar-hmr
공식 코드https://github.com/ssfootball04/generative_pose
공식 코드Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking
https://github.com/stability-ai/generative-models
공식 코드Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
https://github.com/subin-kim-cv/NVP
공식 코드Scalable Neural Video Representations with Learnable Positional Features
https://github.com/sungheonpark/3D_HPE_RN
공식 코드https://github.com/sunnychencool/Anatomy3D
공식 코드Anatomy-aware 3D Human Pose Estimation with Bone-based Pose Decomposition
https://github.com/superstar1225/DensePose_from_WiFi
공식 코드https://github.com/suyz526/zebrapose
공식 코드ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
https://github.com/sxl142/glot
공식 코드Global-to-Local Modeling for Video-based 3D Human Pose and Shape Estimation
https://github.com/syguan96/BOA
공식 코드Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction
https://github.com/syguan96/dynaboa
공식 코드Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptation
https://github.com/taatiteam/motionagformer
공식 코드MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network
https://github.com/take2rohit/stride
공식 코드STRIDE: Single-video based Temporally Continuous Occlusion-Robust 3D Pose Estimation
https://github.com/tang1705/seeclear-neurips24
공식 코드SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-Resolution
https://github.com/tangtao-pku/arts
공식 코드https://github.com/tencentarc/animesr
공식 코드AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos
https://github.com/tencentarc/fastrealvsr
공식 코드Mitigating Artifacts in Real-World Video Super-Resolution Models
https://github.com/tencentarc/instantmesh
공식 코드https://github.com/tho-kn/Ego3DPose
공식 코드Ego3DPose: Capturing 3D Cues from Binocular Egocentric Views
https://github.com/tho-kn/egotap
공식 코드Attention-Propagation Network for Egocentric Heatmap to 3D Pose Lifting
https://github.com/thohemp/6drepnet
공식 코드6D Rotation Representation For Unconstrained Head Pose Estimation
https://github.com/thohemp/6drepnet360
공식 코드Towards Robust and Unconstrained Full Range of Rotation Head Pose Estimation
https://github.com/thunil/TecoGAN
공식 코드Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.