LoomNet: Enhancing Multi-View Image Generation via Latent Space Weaving
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916831762055168 |
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| author | Federico, Giulio Carrara, Fabio Gennaro, Claudio Amato, Giuseppe Di Benedetto, Marco |
| author_facet | Federico, Giulio Carrara, Fabio Gennaro, Claudio Amato, Giuseppe Di Benedetto, Marco |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_05499 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LoomNet: Enhancing Multi-View Image Generation via Latent Space Weaving Federico, Giulio Carrara, Fabio Gennaro, Claudio Amato, Giuseppe Di Benedetto, Marco Computer Vision and Pattern Recognition 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. |
| title | LoomNet: Enhancing Multi-View Image Generation via Latent Space Weaving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.05499 |