Frequent interactions between individuals are a fundamental challenge for
pose estimation algorithms. Current pipelines either use an object detector
together with a pose estimator (top-down approach), or localize all body parts
first and then link them to predict the pose of individuals (bottom-up). Yet,
when individuals closely interact, top-down methods are ill-defined due to
overlapping individuals, and bottom-up methods often falsely infer connections
to distant body parts. Thus, we propose a novel pipeline called bottom-up
conditioned top-down pose estimation (BUCTD) that combines the strengths of
bottom-up and top-down methods. Specifically, we propose to use a bottom-up
model as the detector, which in addition to an estimated bounding box provides
a pose proposal that is fed as condition to an attention-based top-down model.
We demonstrate the performance and efficiency of our approach on animal and
human pose estimation benchmarks. On CrowdPose and OCHuman, we outperform
previous state-of-the-art models by a significant margin. We achieve 78.5 AP on
CrowdPose and 47.2 AP on OCHuman, an improvement of 8.6% and 4.9% over the
prior art, respectively. Furthermore, we show that our method has excellent
performance on non-crowded datasets such as COCO, and strongly improves the
performance on multi-animal benchmarks involving mice, fish and monkeys.

提出了一种新颖的基于 Bottom-up 和 Top-down 方法相结合的姿态估计模型 ——BUCTD，并将该方法应用于动物和人的关键点检测，与之前的现有模型在 CrowdPose 和 OCHuman 数据集上表现相比有了较大改进，并且在非拥挤数据集上的表现也较好。