Estimating human pose and shape from monocular images is a long-standing
problem in computer vision. Since the release of statistical body models, 3D
human mesh recovery has been drawing broader attention. With the same goal of
obtaining well-aligned and physically plausible mesh results, two paradigms
have been developed to overcome challenges in the 2D-to-3D lifting process: i)
an optimization-based paradigm, where different data terms and regularization
terms are exploited as optimization objectives; and ii) a regression-based
paradigm, where deep learning techniques are embraced to solve the problem in
an end-to-end fashion. Meanwhile, continuous efforts are devoted to improving
the quality of 3D mesh labels for a wide range of datasets. Though remarkable
progress has been achieved in the past decade, the task is still challenging
due to flexible body motions, diverse appearances, complex environments, and
insufficient in-the-wild annotations. To the best of our knowledge, this is the
first survey to focus on the task of monocular 3D human mesh recovery. We start
with the introduction of body models and then elaborate recovery frameworks and
training objectives by providing in-depth analyses of their strengths and
weaknesses. We also summarize datasets, evaluation metrics, and benchmark
results. Open issues and future directions are discussed in the end, hoping to
motivate researchers and facilitate their research in this area. A regularly
updated project page can be found at this https URL

本篇文章调查了单目三维人体网格恢复的相关任务，包括身体模型，恢复框架 / 训练目标，数据集评估指标和评测结果，并从优势和劣势方面深入分析了两种范式：优化和回归。作者希望激励研究人员的兴趣，促进该领域的研究。