  Most existing human pose estimation (HPE) methods exploit multi-scale
information by fusing feature maps of four different spatial sizes, \ie $1/4$,
$1/8$, $1/16$, and $1/32$ of the input image. There are two drawbacks of this
strategy: 1) feature maps of different spatial sizes may be not well aligned
spatially, which potentially hurts the accuracy of keypoint location; 2) these
scales are fixed and inflexible, which may restrict the generalization ability
over various human sizes. Towards these issues, we propose an adaptive dilated
convolution (ADC). It can generate and fuse multi-scale features of the same
spatial sizes by setting different dilation rates for different channels. More
importantly, these dilation rates are generated by a regression module. It
enables ADC to adaptively adjust the fused scales and thus ADC may generalize
better to various human sizes. ADC can be end-to-end trained and easily plugged
into existing methods. Extensive experiments show that ADC can bring consistent
improvements to various HPE methods. The source codes will be released for
further research.
