``What I cannot create, I do not understand.'' Human wisdom reveals that creation is one of the highest forms of learning. For example, Diffusion Models have demonstrated remarkable semantic structural and memory capabilities in image generation, denoising, and restoration, which intuitively benefits representation learning. However, current gait networks rarely embrace this perspective, relying primarily on learning by contrasting gait samples under varying complex conditions, leading to semantic inconsistency and uniformity issues. To address these issues, we propose Origins with generative capabilities whose underlying philosophy is that different entities are generated from a unified template, inherently regularizing gait representations within a consistent and diverse semantic space to capture differences accurately. Admittedly, learning this unified template is exceedingly challenging, as it requires the comprehensiveness of the template to encompass gait representations with various conditions. Inspired by Diffusion Models, Origins diffuses the unified template into timestep templates for gait generative modeling, and meanwhile transfers the unified template for gait representation learning. Especially, gait generative modeling and representation learning serve as a unified framework to end-to-end joint training. Extensive experiments on CASIA-B, CCPG, SUSTech1K, Gait3D, GREW and CCGR-MINI demonstrate that Origins performs representation learning within a unified template, achieving superior performance.