| title | SphereFlow: Missing Modality Imputation via Geometric Transport on Hypersphere |
|---|---|
| authors | Huihan Wang, Juncheng Wang, Yuxiang Feng, Wenlong Hou, Guoqi Yu, Chao Xu, Yang Liu, Baigui Sun, Shujun Wang |
| date | 2026-11-03 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2611.04414 |
| upvotes | 1 (Hugging Face Daily Papers, 2026-11-05) |
| links | abs pdf hf code |
| cite | |
| abstract | Missing modality imputation aims to synthesize unobserved modalities from observed ones. Existing methods treat each modality as living on its own latent manifold, forcing the generator to learn a long, unstructured jump between them. We present SphereFlow, which takes a fundamentally different view: by projecting all modalities into a shared hyperspherical latent space via a frozen self-supervised encoder, cross-modal imputation reduces to short-range geometric transport between nearby points on the sphere. Realizing this idea with flow matching, however, exposes two geometric conflicts: the standard Gaussian noise source is catastrophically mismatched with the unit-norm data manifold, and the linear interpolation path departs the sphere surface into a region where the decoder has no training signal. We resolve both with simple, geometry-aware modifications: replacing the noise source with the observed-modality latent so that the velocity field learns only the cross-modal displacement, and training the decoder with a time-weighted loss that extends its domain to the near-surface region traversed by the flow. We prove that this data-as-source formulation reduces the worst-case off-manifold deviation from a quantity that grows with the latent dimension to a small, dimension-independent bound governed by inter-modality similarity. Experiments on BraTS multi-modal MRI imputation and CT-MRI translation demonstrate state-of-the-art generation fidelity across all settings, while downstream brain tumor segmentation with our imputed data closes roughly eighty percent of the gap to the full-modality oracle. Code is available at https://github.com/WTHH031230/SphereFlow. |
bibtex
@misc{wang2026sphereflow,
title={SphereFlow: Missing Modality Imputation via Geometric Transport on Hypersphere},
author={Huihan Wang and Juncheng Wang and Yuxiang Feng and Wenlong Hou and Guoqi Yu and Chao Xu and Yang Liu and Baigui Sun and Shujun Wang},
year={2026},
eprint={2611.04414},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2611.04414}
}