In multi-person 2D pose estimation, the bottom-up methods simultaneously predict poses for all persons, and unlike the top-down methods, do not rely on human detection. However, the SOTA bottom-up methods' accuracy is still inferior compared to the existing top-down methods. This is due to the predicted human poses being regressed based on the inconsistent human bounding box center and the lack of human-scale normalization, leading to the predicted human poses being inaccurate and small-scale persons being missed. To push the envelope of the bottom-up pose estimation, we firstly propose multi-scale training to enhance the network to handle scale variation with single-scale testing, particularly for small-scale persons. Secondly, we introduce dual anatomical centers (i.e., head and body), where we can predict the human poses more accurately and reliably, especially for small-scale persons. Moreover, existing bottom-up methods use multi-scale testing to boost the accuracy of pose estimation at the price of multiple additional forward passes, which weakens the efficiency of bottom-up methods, the core strength compared to top-down methods. By contrast, our multi-scale training enables the model to predict high-quality poses in a single forward pass (i.e., single-scale testing). Our method achieves 38.4\% improvement on bounding box precision and 39.1\% improvement on bounding box recall over the state of the art (SOTA) on the challenging small-scale persons subset of COCO. For the human pose AP evaluation, we achieve a new SOTA (71.0 AP) on the COCO test-dev set with the single-scale testing. We also achieve the top performance (40.3 AP) on OCHuman dataset in cross-dataset evaluation.
翻译:在多人 2D 的估算中,自下而上的方法同时预测所有的人,与自上而下的方法不同,不依赖于人类检测。然而,SOTA自下而上方法的准确性仍然低于现有的自上而下方法。这是因为,根据人与人之间不协调的捆绑箱中心,预测人与人之间的关系会倒退,缺乏人与人之间的正常化,导致预测人与人之间的关系不准确,造成小规模人员错失。为了推推自上而上之的包包包包,我们首先提议进行多级培训,以加强网络,通过单级测试处理规模差异,特别是小规模人员。第二,我们采用双级解剖式中心(即头部和身体),我们可以在其中更准确和更可靠地预测人与人之间的关系。此外,现有的自下而上而上而下的测试方法使用多规模的CO来提高假设的准确性,从而降低自下而上而上之方法的效率,核心力量与自上而下的评估方法相比。相比之下,我们的多级培训也通过SO1 的高级测试,让我们的SLA-SB 的高级测试模式,实现SAL 高级测试。