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arXiv Machine Learning By Kyeongmin Yeo, Minhyuk Sung

MUNITE: Unified Multimodal Latent Inference for Any-to-Any Multimodal Generation

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MUNITE is a latent‑variable framework that unifies encoding and latent generation for any‑to‑any multimodal generation. By treating different amounts of observed evidence as varying inference problems, it models the conditional distribution over a shared latent representation, using a single conditional flow model. The approach employs self‑distillation with conditional flow matching to learn from incomplete data, achieving competitive or superior generation quality and coherence across datasets such as PolyMNIST‑D‑Q, FFHQ64, and image‑text‑audio.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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