用共享潜空间生成16张一致的多视角图像，仅需15秒。
LoomNet: Enhancing Multi-View Image Generation via Latent Space Weaving
- 多视角并行扩散，共享潜空间协同构建视图一致性。
- 15秒生成16张高质多视角图像，优于现有方法。
- 适合需要快速生成多样、一致3D视图的研究者。
从单张图像生成一致的多视角图像仍具挑战性，空间不一致性常导致表面重建中3D网格质量下降。为此，我们提出LoomNet，一种新型多视角扩散架构，通过多次并行应用同一扩散模型，协作构建并利用共享潜空间以实现视图一致性。每个视角推断生成对应新视角的编码，投影至三个正交平面；各平面内所有视角编码融合为单一聚合平面，再通过信息传播与缺失区域插值，整合为统一连贯的解释。最终潜空间用于渲染一致的多视角图像。LoomNet在15秒内生成16张高质量一致图像，在图像质量和重建指标上超越现有最优方法，且能从相同输入生成多样、合理的新增视角。
原文摘要 · Abstract (English)
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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