  We present a new self-supervised approach, SelfPose3d, for estimating 3d
poses of multiple persons from multiple camera views. Unlike current
state-of-the-art fully-supervised methods, our approach does not require any 2d
or 3d ground-truth poses and uses only the multi-view input images from a
calibrated camera setup and 2d pseudo poses generated from an off-the-shelf 2d
human pose estimator. We propose two self-supervised learning objectives:
self-supervised person localization in 3d space and self-supervised 3d pose
estimation. We achieve self-supervised 3d person localization by training the
model on synthetically generated 3d points, serving as 3d person root
positions, and on the projected root-heatmaps in all the views. We then model
the 3d poses of all the localized persons with a bottleneck representation, map
them onto all views obtaining 2d joints, and render them using 2d Gaussian
heatmaps in an end-to-end differentiable manner. Afterwards, we use the
corresponding 2d joints and heatmaps from the pseudo 2d poses for learning. To
alleviate the intrinsic inaccuracy of the pseudo labels, we propose an adaptive
supervision attention mechanism to guide the self-supervision. Our experiments
and analysis on three public benchmark datasets, including Panoptic, Shelf, and
Campus, show the effectiveness of our approach, which is comparable to
fully-supervised methods. Code: https://github.com/CAMMA-public/SelfPose3D.
Video demo: https://youtu.be/GAqhmUIr2E8.
