  Human pose and shape (HPS) estimation with lensless imaging is not only
beneficial to privacy protection but also can be used in covert surveillance
scenarios due to the small size and simple structure of this device. However,
this task presents significant challenges due to the inherent ambiguity of the
captured measurements and lacks effective methods for directly estimating human
pose and shape from lensless data. In this paper, we propose the first
end-to-end framework to recover 3D human poses and shapes from lensless
measurements to our knowledge. We specifically design a multi-scale lensless
feature decoder to decode the lensless measurements through the optically
encoded mask for efficient feature extraction. We also propose a double-head
auxiliary supervision mechanism to improve the estimation accuracy of human
limb ends. Besides, we establish a lensless imaging system and verify the
effectiveness of our method on various datasets acquired by our lensless
imaging system.
