https://github.com/jhacsonmeza/MarkerPose
공식 코드MarkerPose: Robust Real-time Planar Target Tracking for Accurate Stereo Pose Estimation
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MarkerPose: Robust Real-time Planar Target Tracking for Accurate Stereo Pose Estimation
Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition
Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
HDNet: Human Depth Estimation for Multi-Person Camera-Space Localization
Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo
Learning Spatial Context with Graph Neural Network for Multi-Person Pose Grouping
Trajectory Space Factorization for Deep Video-Based 3D Human Pose Estimation
MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds
MC$^2$: Multi-concept Guidance for Customized Multi-concept Generation
Neural Pose Transfer by Spatially Adaptive Instance Normalization
TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On
SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation
LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF
Real-Time Object Pose Estimation with Pose Interpreter Networks
Highly Efficient 3D Human Pose Tracking from Events with Spiking Spatiotemporal Transformer
UniFS: Universal Few-shot Instance Perception with Point Representations
Mamba-Diffusion Model with Learnable Wavelet for Controllable Symbolic Music Generation
Composer Style-specific Symbolic Music Generation Using Vector Quantized Discrete Diffusion Models
SpecRef: A Fast Training-free Baseline of Specific Reference-Condition Real Image Editing
Capability-aware Prompt Reformulation Learning for Text-to-Image Generation
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
Recurrent Video Restoration Transformer with Guided Deformable Attention
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.