We present a novel approach to human pose and shape estimation through a lensless imaging system. Existing methods for human pose and shape estimation are done with RGB images captured by conventional cameras. While, lensless imaging system can have a smaller size, simpler structure and stronger privacy protection attributes, and hence can be adapted to a variety of complex environments. In this paper, we propose the first end-to-end framework to recover 3D human poses and shapes from lensless measurements, to our best knowledge. We specifically design a multi-level lensless feature decoder to decode the lensless measurements base on mask-encoder to obtain more efficient features. We also propose a dual auxiliary supervision mechanism to improve the accuracy of estimation of human limb ends. Besides, we establish a lenless imaging system and verify the effectiveness of our methed on various datasets acquired by our lensless imaging system. The code and dataset will be will be available for research purposes.