We present a deployment friendly, fast bottom-up framework for multi-person
3D human pose estimation. We adopt a novel neural representation of
multi-person 3D pose which unifies the position of person instances with their
corresponding 3D pose representation. This is realized by learning a generative
pose embedding which not only ensures plausible 3D pose predictions, but also
eliminates the usual keypoint grouping operation as employed in prior bottom-up
approaches. Further, we propose a practical deployment paradigm where paired 2D
or 3D pose annotations are unavailable. In the absence of any paired
supervision, we leverage a frozen network, as a teacher model, which is trained
on an auxiliary task of multi-person 2D pose estimation. We cast the learning
as a cross-modal alignment problem and propose training objectives to realize a
shared latent space between two diverse modalities. We aim to enhance the
model's ability to perform beyond the limiting teacher network by enriching the
latent-to-3D pose mapping using artificially synthesized multi-person 3D scene
samples. Our approach not only generalizes to in-the-wild images, but also
yields a superior trade-off between speed and performance, compared to prior
top-down approaches. Our approach also yields state-of-the-art multi-person 3D
pose estimation performance among the bottom-up approaches under consistent
supervision levels.

通过使用新颖的神经表示，我们提出了一种部署友好的、快速的自下而上的多人三维人体姿态估计框架；进一步，我们基于跨模态的对齐问题，提出了一种实用的部署范式，使得我们的模型不仅能够处理现实世界的图像，而且在速度与性能之间实现了更好的平衡。