arXiv:2610.27564v1 Announce Type: cross Abstract: Generating high-fidelity, city-scale 3D environments plays a crucial role in applications such as virtual reality, gaming, and urban digital twins. H...
Generating high-fidelity, city-scale 3D environments plays a crucial role in applications such as virtual reality, gaming, and urban digital twins. However, the scarcity of real-world city-scale 3D tr...
SpatialCrafter introduces a two‑stage framework for single‑image world modeling that first generates a global 3D proxy using a Point‑anchored Sparse Structure Flow module, then refines appearance with a Generative Deferred Refiner built on a video diffusion model. The method incorporates Parallel Geometry Injection and Proxy‑Aware Corruption training to integrate the proxy without disrupting the pretrained generative manifold, and it is evaluated on a newly constructed dataset of 115K scenes. Experiments demonstrate that SpatialCrafter outperforms existing approaches, reducing long‑term drift and maintaining consistency under rapid camera motion and extreme viewpoints.
arXiv:2606. 07117v1 Announce Type: cross Abstract: This paper presents Native3D, the first end-to-end 3D scene generation framework that completely bypasses 2D intermediate representations.
MeSS (Mesh-based Scene Synthesis) is a method that generates high‑quality, style‑consistent outdoor scenes using city mesh models as geometric priors. The pipeline first creates geometrically consistent sparse views with Cascaded Outpainting ControlNets, then densifies intermediate views via AGInpaint, and finally removes visual inconsistencies with GCAlign. Simultaneously, a 3D Gaussian Splatting scene is reconstructed from the mesh, enabling diverse rendering styles after synthesis.
arXiv:2608.28895v1 Announce Type: new Abstract: We introduce ReconSplat, a feed-forward model for 3D scene reconstruction that aims to address the longstanding trade-off between plausible view genera...
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.
arXiv:2609.23169v1 Announce Type: new Abstract: High-quality texture generation is essential for creating realistic and production-ready 3D assets. Recent multi-view diffusion methods have shown prom...
WildShadowRemover is a framework that adapts a pretrained video diffusion model for robust in-the-wild video shadow removal using LoRA fine-tuning. It augments the frozen VAE decoder with a detail injection module and introduces a shadow‑mask‑guided frequency‑decomposed modulation module to restore high‑frequency textures while suppressing shadow artifacts, with monocular depth priors providing geometry‑aware guidance. The authors also create WildShadow, a large‑scale paired video shadow removal dataset, and show that their method outperforms existing approaches in shadow removal quality, temporal consistency, and generalization across challenging real‑world scenarios.
arXiv:2605.12957v2 Announce Type: replace Abstract: Recent developments in generative models and large-scale datasets have substantially advanced 3D world generation, facilitating a broad range of do...
The paper introduces ESC, a framework that improves 3D-consistent novel view synthesis from a single 2D image by jointly generating a target view and its depth map. By conditioning the diffusion model on the source image, source depth, and target camera rays, ESC forces the model to reason in 3D even under extreme viewpoint changes. A per‑pixel depth consistency constraint is added to enforce spatial fidelity, resulting in superior visual accuracy and spatial alignment compared to current state‑of‑the‑art methods.
GS-PI introduces an optimization‑decoupled framework that transforms Gaussian Splatting (GS) assets into physically based rendering (PBR) compatible Gaussian assets. By treating PBR material generation as a geometry‑conditioned diffusion process on 3D point clouds, it achieves multi‑view consistency and avoids the pixel‑correspondence problems of 2D diffusion. The method employs a multi‑scale cross‑view conditioning mechanism—combining global semantic priors, photometric cues, and spatial view‑direction signals—to prevent specular highlights from baking into intrinsic colors, and then distills the predicted attributes back into a fully relightable PBR‑GS asset without requiring proxy meshes.