Conventional approaches to human mesh recovery predominantly employ a
region-based strategy. This involves initially cropping out a human-centered
region as a preprocessing step, with subsequent modeling focused on this
zoomed-in image. While effective for single figures, this pipeline poses
challenges when dealing with images featuring multiple individuals, as
different people are processed separately, often leading to inaccuracies in
relative positioning. Despite the advantages of adopting a whole-image-based
approach to address this limitation, early efforts in this direction have
fallen short in performance compared to recent region-based methods. In this
work, we advocate for this under-explored area of modeling all people at once,
emphasizing its potential for improved accuracy in multi-person scenarios
through considering all individuals simultaneously and leveraging the overall
context and interactions. We introduce a new model with a streamlined
transformer-based design, featuring three critical design choices: multi-scale
feature incorporation, focused attention mechanisms, and relative joint
supervision. Our proposed model demonstrates a significant performance
improvement, surpassing state-of-the-art region-based and whole-image-based
methods on various benchmarks involving multiple individuals.

通过一种面向整体图像的方法，我们提出了一种新的模型设计，其中包括多尺度特征融合、关注机制和相对关节监督，该模型在涉及多个人的各种基准测试中显示出显著的性能提升，超过了最先进的基于区域和基于整体图像的方法。