Simple Baselines for Human Pose Estimation and Tracking
arXiv:1804.06208
Abstract
There has been significant progress on pose estimation and increasing interests on pose tracking in recent years. At the same time, the overall algorithm and system complexity increases as well, making the algorithm analysis and comparison more difficult. This work provides simple and effective baseline methods. They are helpful for inspiring and evaluating new ideas for the field. State-of-the-art results are achieved on challenging benchmarks. The code will be available at https://github.com/leoxiaobin/pose.pytorch.
arXiv commentAccepted by ECCV 2018
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Cited by in corpus (10)
- Rethinking on Multi-Stage Networks for Human Pose Estimation
- Real-Time Multi-View 3D Human Pose Estimation using Semantic Feedback to Smart Edge Sensors
- SHaRPose: Sparse High-Resolution Representation for Human Pose Estimation
- Person-in-WiFi: Fine-grained Person Perception using WiFi
- Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation
- A Context-and-Spatial Aware Network for Multi-Person Pose Estimation
- X-model: Improving Data Efficiency in Deep Learning with A Minimax Model
- Regressive Domain Adaptation for Unsupervised Keypoint Detection
- Explicit Spatiotemporal Joint Relation Learning for Tracking Human Pose
- Multi-Level Network for High-Speed Multi-Person Pose Estimation