LoomNet: Enhancing Multi-View Image Generation via Latent Space Weaving

• URL: http://arxiv.org/abs/2507.05499v1
• Date: Mon, 07 Jul 2025 21:46:50 GMT
• Title: LoomNet: Enhancing Multi-View Image Generation via Latent Space Weaving
• Authors: Giulio Federico, Fabio Carrara, Claudio Gennaro, Giuseppe Amato, Marco Di Benedetto,
• Abstract summary: LoomNet is a novel multi-view diffusion architecture that produces coherent images by applying the same diffusion model multiple times.<n>In experiments, LoomNet outperforms state-of-the-art methods on both image quality and reconstruction metrics.
• Score: 7.999454304974351
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating consistent multi-view images from a single image remains challenging. Lack of spatial consistency often degrades 3D mesh quality in surface reconstruction. To address this, we propose LoomNet, a novel multi-view diffusion architecture that produces coherent images by applying the same diffusion model multiple times in parallel to collaboratively build and leverage a shared latent space for view consistency. Each viewpoint-specific inference generates an encoding representing its own hypothesis of the novel view from a given camera pose, which is projected onto three orthogonal planes. For each plane, encodings from all views are fused into a single aggregated plane. These aggregated planes are then processed to propagate information and interpolate missing regions, combining the hypotheses into a unified, coherent interpretation. The final latent space is then used to render consistent multi-view images. LoomNet generates 16 high-quality and coherent views in just 15 seconds. In our experiments, LoomNet outperforms state-of-the-art methods on both image quality and reconstruction metrics, also showing creativity by producing diverse, plausible novel views from the same input.

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