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LF-MultiDiffusion is a training‑free method for generating spherical panoramas that extends MultiDiffusion by adding linear projections between target and reference image spaces. It reformulates latent aggregation as a regularized least‑squares problem and solves it with a Krylov‑based iterative solver during denoising, enabling denser and more natural mappings. The approach reduces the number of generator evaluations, improves inference speed by 15.36×, and yields better visual quality, text alignment, and panoramic consistency compared to the strongest training‑free baseline.
Multi-view diffusion models have shown strong performance in scenes with strong geometric priors and sparse semantics, such as indoor rooms or simple outdoor environments (e.g., fields, courtyards). H...
arXiv:2609.09890v1 Announce Type: new Abstract: Multi-view diffusion models have shown strong performance in scenes with strong geometric priors and sparse semantics, such as indoor rooms or simple o...
Scaling 3D Gaussian Splatting (3DGS) to large outdoor scenes is costly in both data acquisition and computation. Adopting panoramic images with equirectangular projection (ERP) can reduce capture effort via their full $360^{\circ}$ field of view, yet the resulting omnipresent visibility invalidates existing partitioning strategies that rely on local camera frustums, causing block-wise optimization to degenerate into global training.
We study the challenging problem of novel view video synthesis from single images or monocular videos. Existing methods, which operate under the assumption that pre-trained video models lack native novel view synthesis capability and enforce view alignment via camera conditioning, task-specific fine-tuning, or stepwise hard denoising guidance, often suffer from artifacts and compromised global scene consistency.
JanusMesh introduces a fast, training‑free framework for creating 3D visual illusion meshes that reveal different semantics from various viewpoints. The method splits generation into two stages: a cross‑space dual‑branch denoising process that aligns 3D latents with CLIP guidance and blends Signed Distance Fields for seamless geometry, followed by a view‑conditioned texture synthesis module that aggregates 2D diffusion priors onto the fused mesh. Experiments show that JanusMesh produces highly realistic, dual‑semantic 3D illustrations in only 3–5 minutes, outperforming prior approaches in geometric integrity, semantic recognizability, and efficiency.