XmoPipe: A Pipeline for Large-Scale In-the-Wild Human Motion Dataset Construction

XmoPipe is a scalable pipeline that constructs large-scale human motion datasets by extracting 3D body and facial motion from unconstrained online videos, combined with automated textual descriptions. The system demonstrates that motion models trained on this in-the-wild data achieve performance comparable to traditional marker-based motion capture datasets while offering superior scalability and diversity.

XmoPipe addresses a fundamental bottleneck in motion AI development: the scarcity of large-scale, diverse human motion data. Traditional marker-based motion capture requires expensive equipment, controlled environments, and specialized expertise, limiting dataset scale and real-world motion diversity. This pipeline leverages recent advances in monocular motion capture and video-language models to democratize motion dataset construction, enabling researchers to build targeted collections from internet video sources using simple keyword queries.

The broader context reflects an industry-wide shift toward leveraging abundant unlabeled internet data rather than relying on constrained laboratory settings. Computer vision and language models have benefited enormously from web-scale training data; motion understanding is now following this trajectory. By automating the extraction of 3D motion representations and generating semantic descriptions automatically, XmoPipe reduces manual annotation overhead while maintaining quality standards comparable to gold-standard datasets.

For the AI industry, this work has substantial implications for motion synthesis applications spanning animation, robotics, sports analytics, and embodied AI. Developers can now access or construct large motion datasets without prohibitive infrastructure costs, accelerating innovation in motion generation and understanding. The demonstrated cross-dataset generalization suggests models trained on in-the-wild data transfer effectively to downstream applications, validating the pipeline's practical utility.

Looking ahead, the critical challenges involve scaling annotation quality with dataset size, handling ambiguous or low-quality video sources, and addressing potential copyright or ethical concerns with using internet video. If these issues are resolved, XmoPipe-style approaches could become standard infrastructure for motion AI research, similar to how web scraping enabled modern language models.

• →XmoPipe enables large-scale motion dataset construction from internet videos, reducing reliance on expensive marker-based motion capture systems.
• →Models trained on automatically extracted in-the-wild motion data achieve comparable performance to traditional motion capture datasets.
• →The pipeline demonstrates strong cross-dataset generalization, indicating practical applicability to diverse downstream tasks.
• →Automated video retrieval, 3D motion extraction, and textual description generation reduce manual annotation overhead significantly.
• →This approach follows industry trends of leveraging abundant unlabeled data to overcome dataset scarcity bottlenecks in specialized domains.