Papers with Code 아카이브
3D Human Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
PhysX: Physical-Grounded 3D Asset Generation
DreamArt: Generating Interactable Articulated Objects from a Single Image
Systematic Comparison of Projection Methods for Monocular 3D Human Pose Estimation on Fisheye Images
DreamJourney: Perpetual View Generation with Video Diffusion Models
Compressed Video Super-Resolution based on Hierarchical Encoding
PoseGRAF: Geometric-Reinforced Adaptive Fusion for Monocular 3D Human Pose Estimation
FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution
ICME 2025 Grand Challenge on Video Super-Resolution for Video Conferencing
EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligence
LiftVSR: Lifting Image Diffusion to Video Super-Resolution via Hybrid Temporal Modeling with Only 4$\times$RTX 4090s
UA-Pose: Uncertainty-Aware 6D Object Pose Estimation and Online Object Completion with Partial References
A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-Resolution
DualX-VSR: Dual Axial Spatial$\times$Temporal Transformer for Real-World Video Super-Resolution without Motion Compensation
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation
A Survey of Deep Learning Video Super-Resolution
Omnidirectional Video Super-Resolution using Deep Learning
AdaHuman: Animatable Detailed 3D Human Generation with Compositional Multiview Diffusion
Automatic Scene Generation: State-of-the-Art Techniques, Models, Datasets, Challenges, and Future Prospects
UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Space
Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention
EasyInsert: A Data-Efficient and Generalizable Insertion Policy
DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution
Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization
Constructing a 3D Town from a Single Image
Blind Restoration of High-Resolution Ultrasound Video
Hunyuan-Game: Industrial-grade Intelligent Game Creation Model
Personalize Your Gaussian: Consistent 3D Scene Personalization from a Single Image
UPTor: Unified 3D Human Pose Dynamics and Trajectory Prediction for Human-Robot Interaction
Multi-Resolution Haar Network: Enhancing human motion prediction via Haar transform
PoseBench3D: A Cross-Dataset Analysis Framework for 3D Human Pose Estimation
3D-Fixup: Advancing Photo Editing with 3D Priors
GRNN:Recurrent Neural Network based on Ghost Features for Video Super-Resolution
Super-Resolution Generative Adversarial Networks based Video Enhancement
DiffLocks: Generating 3D Hair from a Single Image using Diffusion Models
SOAP: Style-Omniscient Animatable Portraits
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.