LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network
LPFormer introduces an end-to-end framework for 3D human pose estimation using only LiDAR input and 3D annotations, achieving state-of-the-art performance on the Waymo Open Dataset.
LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network
LPFormer는 LiDAR를 사용한 3D 인간 자세 추정을 위한 첫 번째 end-to-end 프레임워크로, LiDAR만을 입력으로 사용하고 3D 주석만을 훈련에 사용합니다.
LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network
LPFormer ermöglicht präzise 3D-Human-Pose-Schätzungen ausschließlich mit LiDAR-Eingabe und 3D-Annotationen.
Disentangled Diffusion-Based 3D Human Pose Estimation with Hierarchical Spatial and Temporal Denoiser
Die Disentangled Diffusion-basierte 3D Human Pose Estimation mit Hierarchical Spatial und Temporal Denoiser verbessert die Genauigkeit und Leistung von 3D-Human-Pose-Schätzungen durch die Integration hierarchischer Informationen.
Enhancing 3D Human Pose Estimation with Orientation and Semi-supervised Training
The introduction of a 2D-to-3D pose lifting method that incorporates bone joint orientations, significantly enhancing model performance, and the development of a semi-supervised training approach to overcome the scarcity of orientation training data.
Probabilistic Restoration with Adaptive Noise Sampling for Efficient 3D Human Pose Estimation
A novel framework called PRPose that seamlessly extends lightweight single-hypothesis 3D human pose estimation models to the multi-hypothesis setting, significantly enhancing computational efficiency while maintaining state-of-the-art accuracy.
Multi-hop Graph Transformer Network for Accurate 3D Human Pose Estimation from 2D Inputs
The proposed Multi-hop Graph Transformer Network (MGT-Net) effectively captures both local and global dependencies in human body movements by leveraging multi-head self-attention and multi-hop graph convolutions with disentangled neighborhoods, enabling accurate 3D human pose estimation from 2D inputs.
人体3次元姿勢推定のためのグラフMLP型アーキテクチャ
グラフ構造を持つ人体スケルトンデータに対して、MLPとGCNを組み合わせたグラフMLP型アーキテクチャを提案し、局所的および大域的な特徴を効果的に学習する。
© 2024 by Linnk AI