Recent years have witnessed rapid progress in monocular human mesh recovery.Despite their impressive performance on public benchmarks, existing methods are vulnerable to unusual poses, which prevents them from deploying to challenging scenarios such as dance and martial arts.This issue is mainly attributed to the domain gap induced by the data scarcity in relevant cases.Most existing datasets are captured in constrained scenarios and lack samples of such complex movements. For this reason, we propose a data collection pipeline comprising automatic crawling, precise annotation, and hardcase mining. Based on this pipeline, we establish a large dataset in a short time.The dataset, named HardMo, contains 7M images along with precise annotations covering 15 categories of dance and 14 categories of martial arts.Empirically, we find that the prediction failure in dance and martial arts is mainly characterized by the misalignment of hand-wrist and foot-ankle.To dig deeper into the two hardcases, we leverage the proposed automatic pipeline to filter collected data and construct two subsets named HardMo-Hand and HardMo-Foot. Extensive experiments demonstrate the effectiveness of the annotation pipeline and the data-driven solution to failure cases.Specifically, after being trained on HardMo, HMR, an early pioneering method, can even outperform the current state of the art, 4DHumans, on our benchmarks.