mlsanityData to 14 Oct 2026

RapidMV: Leveraging Spatio-Angular Representations for Efficient and Consistent Text-to-Multi-View Synthesis

arXiv 2509.24410 ↗First posted 29 Sep 2025cs.CVcited by 0

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Abstract (arXiv)

Generating synthetic multi-view images from a text prompt is an essential bridge to generating synthetic 3D assets. In this work, we introduce RapidMV, a novel text-to-multi-view generative model that can produce 32 multi-view synthetic images in just around 5 seconds. In essence, we propose a novel spatio-angular latent space, encoding both the spatial appearance and angular viewpoint deviations into a single latent for improved efficiency and multi-view consistency. We achieve effective training of RapidMV by strategically decomposing our training process into multiple steps. We demonstrate that RapidMV outperforms existing methods in terms of consistency and latency, with competitive quality and text-image alignment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 40 of its 52 references that are in the map. First posted on arXiv 29 Sep 2025.

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References · 40

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28 more
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