  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 bodyparts. 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 48.5 AP on OCHuman, an improvement of 8.6% and 7.8% over the
prior art, respectively. Furthermore, we show that our method strongly improves
the performance on multi-animal benchmarks involving fish and monkeys. The code
is available at https://github.com/amathislab/BUCTD
