Integral Human Pose Regression
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State-of-the-art human pose estimation methods are dominated by complex multi-stage architectures using a heat map representation. The representation has a few issues in nature, such as no end-to-end training, quantization error and sensitivity to multiple modes. While pose estimation is essentially a regression problem, existing regression approaches are not as effective. This work proposes an integral regression approach that unifies the heat map representation and joint regression, thus possessing the merits of both paradigms. It is differentiable and allows end-to-end training. It is simple, fast, and compatible with any heat map based methods. Comprehensive experiments verify its efficacy. Building upon simple baseline networks, its performance on benchmarks is strong and competitive to state-of-the-art.
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