  3D human pose and shape estimation (a.k.a. "human mesh recovery") has
achieved substantial progress. Researchers mainly focus on the development of
novel algorithms, while less attention has been paid to other critical factors
involved. This could lead to less optimal baselines, hindering the fair and
faithful evaluations of newly designed methodologies. To address this problem,
this work presents the first comprehensive benchmarking study from three
under-explored perspectives beyond algorithms. 1) Datasets. An analysis on 31
datasets reveals the distinct impacts of data samples: datasets featuring
critical attributes (i.e. diverse poses, shapes, camera characteristics,
backbone features) are more effective. Strategical selection and combination of
high-quality datasets can yield a significant boost to the model performance.
2) Backbones. Experiments with 10 backbones, ranging from CNNs to transformers,
show the knowledge learnt from a proximity task is readily transferable to
human mesh recovery. 3) Training strategies. Proper augmentation techniques and
loss designs are crucial. With the above findings, we achieve a PA-MPJPE of
47.3 mm on the 3DPW test set with a relatively simple model. More importantly,
we provide strong baselines for fair comparisons of algorithms, and
recommendations for building effective training configurations in the future.
Codebase is available at http://github.com/smplbody/hmr-benchmarks
