Silhouette-based Gait Foundation Model

Gait patterns play a critical role in human identification andhealthcare analytics, yet current progress remains constrained by small,narrowly designed models that fail to scale or generalize. Building a uni-fied gait foundation model requires addressing two longstanding barriers:(a) Scalability – Why have gait models historically failed to follow em-pirical scaling trends? (b) Generalization – Can one model serve thediverse gait tasks that have traditionally been studied in isolation? Weintroduce FoundationGait, the first scalable, self-supervised pretrain-ing framework for vision-based gait understanding. Its largest versionhas nearly 0.13 billion parameters and is pretrained on 12 public gaitdatasets comprising over 2 million walking sequences. Extensive exper-iments demonstrate that FoundationGait, with or without fine-tuning,performs robustly across a wide spectrum of gait datasets, conditions,tasks (e.g., human identification, scoliosis screening, depression predic-tion, and attribute estimation), and even input modality. Notably, itachieves 48.0% self-supervised rank-1 accuracy on the challenging in-the-wild Gait3D dataset (1,000 test subjects) and 64.5% on the largestin-the-lab OU-MVLP dataset (5,000+ test subjects), setting a new mile-stone in robust gait recognition. These results establish FoundationGaitas a strong and versatile foundation for future gait research. All codeand models: https://github.com/ShiqiYu/OpenGait.