  Despite the impressive performance of vision-based pose estimators, they
generally fail to perform well under adverse vision conditions and often don't
satisfy the privacy demands of customers. As a result, researchers have begun
to study tactile sensing systems as an alternative. However, these systems
suffer from noisy and ambiguous recordings. To tackle this problem, we propose
a novel solution for pose estimation from ambiguous pressure data. Our method
comprises a spatio-temporal vision transformer with an encoder-decoder
architecture. Detailed experiments on two popular public datasets reveal that
our model outperforms existing solutions in the area. Moreover, we observe that
increasing the number of temporal crops in the early stages of the network
positively impacts the performance while pre-training the network in a
self-supervised setting using a masked auto-encoder approach also further
improves the results.
