SMPLer-X is a human pose and shape estimation model based on big data and large models. It can unify the capture of body, hand, and facial movements and boasts wide applicability. Through systematic research on datasets from 32 different scenarios, the model optimizes training schemes and dataset selection, leading to a significant enhancement in EHPS capabilities. SMPLer-X employs Vision Transformer for model expansion and utilizes a fine-tuning strategy to transform it into an expert model, further improving performance. The model has demonstrated outstanding performance in multiple benchmark tests, including AGORA (107.2 mm NMVE), UBody (57.4 mm PVE), EgoBody (63.6 mm PVE), and EHF (62.3 mm PVE without finetuning). SMPLer-X's strength lies in its ability to handle diversified data sources, exhibiting excellent generalization capability and transferability.