  DensePose estimation task is a significant step forward for enhancing user
experience computer vision applications ranging from augmented reality to cloth
fitting. Existing neural network models capable of solving this task are
heavily parameterized and a long way from being transferred to an embedded or
mobile device. To enable Dense Pose inference on the end device with current
models, one needs to support an expensive server-side infrastructure and have a
stable internet connection. To make things worse, mobile and embedded devices
do not always have a powerful GPU inside. In this work, we target the problem
of redesigning the DensePose R-CNN model's architecture so that the final
network retains most of its accuracy but becomes more light-weight and fast. To
achieve that, we tested and incorporated many deep learning innovations from
recent years, specifically performing an ablation study on 23 efficient
backbone architectures, multiple two-stage detection pipeline modifications,
and custom model quantization methods. As a result, we achieved $17\times$
model size reduction and $2\times$ latency improvement compared to the baseline
model.
