  The increasing availability of video recordings made by multiple cameras has
offered new means for mitigating occlusion and depth ambiguities in pose and
motion reconstruction methods. Yet, multi-view algorithms strongly depend on
camera parameters; particularly, the relative transformations between the
cameras. Such a dependency becomes a hurdle once shifting to dynamic capture in
uncontrolled settings. We introduce FLEX (Free muLti-view rEconstruXion), an
end-to-end extrinsic parameter-free multi-view model. FLEX is extrinsic
parameter-free (dubbed ep-free) in the sense that it does not require extrinsic
camera parameters. Our key idea is that the 3D angles between skeletal parts,
as well as bone lengths, are invariant to the camera position. Hence, learning
3D rotations and bone lengths rather than locations allows predicting common
values for all camera views. Our network takes multiple video streams, learns
fused deep features through a novel multi-view fusion layer, and reconstructs a
single consistent skeleton with temporally coherent joint rotations. We
demonstrate quantitative and qualitative results on three public datasets, and
on synthetic multi-person video streams captured by dynamic cameras. We compare
our model to state-of-the-art methods that are not ep-free and show that in the
absence of camera parameters, we outperform them by a large margin while
obtaining comparable results when camera parameters are available. Code,
trained models, and other materials are available on our project page.

Computer Vision and Pattern Recognition;Graphics;Machine Learning