Papers
  • Initial page
  • SIGGRAPH 2017
    • Decomposing Images into Layers via RGB-space Geometry
    • Image-based Reconstruction of Wire Art
    • Bounding Proxies for Shape Approximation
    • GRASS: Generative Recursive Autoencoders for Shape Structures
    • DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling
    • Real-Time User-Guided Image Colorization with Learned Deep Priors
    • Spectral Remapping for Image Downscaling
  • CVPR 2017
    • Learning Shape Abstractions by Assembling Volumetric Primitives
    • Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Netwo
    • Deep Image Matting
    • Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
    • DeMoN: Depth and Motion Network for Learning Monocular Stereo
    • A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus Estimation
    • One-Shot Video Object Segmentation
  • SIGGRAPH ASIA 2017
    • Deep Reverse Tone Mapping
    • HDR image reconstruction from a single exposure using deep CNNs
    • BigSUR: Large-scale Structured Urban Reconstruction
    • Adaptive Synthesis of Indoor Scenes via Activity-Associated Object Relation Graphs
    • Robust Structure Simplification for Hex Re-meshing
    • Seamless: Seam erasure and seam-aware decoupling of shape from mesh resolution
    • ComplementMe: Weakly-Supervised Component Suggestions for 3D Modeling
Powered by GitBook
On this page
  • Abstract
  • 问题描述
  • 主要贡献
  • Main Effect/Pipeline
  • 算法流程
  • 网络结构
  • Results
  • 正面
  • 反面
  1. CVPR 2017

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://arxiv.org/pdf/1611.08050.pdf

PreviousDeep Image MattingNextDeMoN: Depth and Motion Network for Learning Monocular Stereo

Last updated 6 years ago

Keyword: Analyzing Humans

Abstract

问题描述

  • 自底向上的实时多人肢体目标跟踪，提出了用关联向量的方式精确连接人物关节。

主要贡献

  • 设计了一个用于检测人物肢体节点和节点关联性的网络结构

  • 设计了一个高效的贪心算法用于解析人物的肢体动作

  • 给出了一个显式非参数的关联编码表示人体四肢的位置和方向

Main Effect/Pipeline

算法流程

网络结构

Results

正面

反面