[Submitted on 24 Sep 2026]
Title:Atlases Are Already Inside: Recovering Population Templates from Pretrained Diffusion Models
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Abstract:We present a new inference-time sampler for diffusion models that gives a pretrained model a capability it was never trained for: constructing the atlas of the population it synthesizes. The sampler converges from every random seed to the population's central anatomy, which we call the \emph{intrinsic atlas}. The advantage is threefold. (1) It requires no retraining. A diffusion model that has already learned a coherent population, including the released ones, yields its atlas in a single inference pass without involving deformable registration. (2) It applies to multiple domains, such as brain MRI, chest X-ray, faces, and 3D shapes. (3) It extends to subpopulations. One age-conditioned model gives an atlas at any age in its training range, and the resulting family reproduces the CSF expansion of healthy aging. Evaluated as a registration target, the intrinsic atlas is best or second-best on every dataset against classical and learned templates, and the most central template on held-out brain MRI cohorts. Atlas construction can be reframed as a byproduct of generative modeling: a diffusion model is a learned representation of population structure, and the atlas is what it already contains.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.30566 [cs.CV]
(or arXiv:2609.30566v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.30566
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jian Shi [view email] [v1] Thu, 24 Sep 2026 21:21:52 UTC (11,463 KB)
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