UniPose: Unified Human Pose Estimation in Single Images and Videos
網絡結構如上如所示 圖片經過ResNet-101網絡，經過WASP模塊，在經過Decoder,得到關鍵點的特徵圖heatmap，每一個heatmap對應一個關節。在通過雙線性差值回覆原始的分辨率。在通過局部最大值操作定位最後的關鍵點。 創新：WASP模塊：瀑布結構 WASP模塊學習了ASPP配置的大的感受野和級聯方式。參考了 ASPP,級聯和Res2Net的結構。 與ASPP和Res2N
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- 1. 3D Human Pose Estimation = 2D Pose Estimation + Matching(2017)
- 2. 【轉】DensePose: Dense Human Pose Estimation In The Wild
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相關文章
- 1. 3D Human Pose Estimation = 2D Pose Estimation + Matching(2017)
- 2. 【轉】DensePose: Dense Human Pose Estimation In The Wild
- 3. 論文閱讀：《Flowing ConvNets for Human Pose Estimation in Videos》ICCV 2015
- 4. DensePose: Dense Human Pose Estimation In The Wild
- 5. Fast Human Pose Estimation
- 6. Paper Reading:Real-time human pose recognition in parts from single depth images
- 7. (20) Recurrent Human Pose Estimation
- 8. Human Pose Estimation using Global and Local Normalization
- 9. Simple Baselines for Human Pose Estimation and Tracking
- 10. VIBE: Video Inference for Human Body Pose and Shape Estimation