Luce is a 3D representation that unifies geometry and physically based rendering (PBR) materials within a voxelized multimodal Gaussian cloud, using dedicated Gaussian primitives for each modality. A variational autoencoder compresses this representation into a unified material‑aware latent space, which a rectified‑flow transformer generates from a single image conditioned on multi‑layer features from a pretrained image encoder. The latent decodes into relightable PBR Gaussians and an optional textured mesh with a tangent‑space normal map, achieving state‑of‑the‑art single‑image‑to‑3D generation on Toys4K and improving CLIP image‑alignment scores on a benchmark of AI‑generated images.
arXiv:2605.05155v4 Announce Type: replace-cross Abstract: As 3D Gaussian Splatting (3DGS) gains attention in immersive media and digital content creation, assessing the aesthetics of 3D scenes become...
arXiv:2606. 27923v1 Announce Type: cross Abstract: We present Home3D 1.
The paper introduces a fusion‑aware hierarchical Gaussian patch representation that enables direct class‑guided generation of 3D Gaussian Splatting (3DGS) objects. By decomposing irregular Gaussian sets into canonical local patches and encoding them as structured tokens, the method fuses global class semantics with patch‑level geometry, appearance, spatial correspondence, and rendering‑sensitive cues. A structure‑aware rectified flow model, conditioned on patch positions and coupled with global‑local velocity prediction and density‑aware weighting, produces class‑conditioned 3DGS objects within seconds, achieving more coherent geometry, sharper local details, and better multi‑view consistency than baseline models.
arXiv:2606. 29379v1 Announce Type: cross Abstract: Gaussian splatting (GS) has garnered significant attention in VR/AR and digital content creation due to its explicit parameterization and efficient rendering capabilities.
SUPRA-Tex is a new method for single‑image 3D generation that dresses a 3D mesh with relightable materials while preserving the visual details of a reference image. It decouples visible‑material estimation, surface alignment, and observation‑guided 3D completion, allowing a 3D generator trained on limited data to focus on preserving visible evidence and completing unobserved surfaces. The approach uses an MLLM‑based image editor to predict lighting‑separated PBR maps, projects them onto the visible surface, encodes them with a pretrained material VAE, and employs an interleaved flow transformer to generate complete material latents, achieving state‑of‑the‑art performance on geometry‑conditioned texturing and end‑to‑end asset generation.
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.
arXiv:2606.27223v3 Announce Type: replace Abstract: Gaussian Splatting has been recently explored for satellite 3D reconstruction, demonstrating flexibility and efficiency in representing radiometric...
Gaussian Splatting has been recently explored for satellite 3D reconstruction, demonstrating flexibility and efficiency in representing radiometrically diverse satellite scenes. However, the limited top viewpoint of satellite imagery results in insufficient supervision on building facades, leaving surface holes and degraded visual fidelity.
Hi3D 3.0, also called Twinkle3D, is an image‑to‑3D generation system that produces watertight triangle meshes at a resolution of 2048³. It improves on previous methods by maintaining high representational precision while fixing surface quality and non‑watertight issues, scaling diffusion generation to 300K geometric tokens, strengthening global shape and local detail in a single stage, and adding a fine‑grained image‑3D cross‑modal interaction to better recover object‑specific structures such as inscriptions. The system outperforms four commercial competitors, recovering 82.1 % of inscribed characters with 98.2 % precision versus 21.7 % recall for the best rival.
arXiv:2605.10360v3 Announce Type: replace Abstract: While novel view synthesis (NVS) for dynamic scenes has seen significant progress, reconstructing temporally consistent geometric surfaces remains...
arXiv:2606. 31637v1 Announce Type: cross Abstract: Intrinsic decomposition which expresses image colors as the product of diffuse albedo and shading, possibly augmented with view-dependent residuals has a long history in image editing as it enables the modification of object colors and textures without altering lighting.
The paper introduces the Geometry‑Native Autoencoder (GAE), a compact latent space that can be decoded into appearance, depth, camera parameters, and point maps, enabling 3D‑consistent world generation. By reparameterizing a geometry foundation model’s features, GAE replaces traditional appearance‑centric latents and improves visual quality and 3D coherence, achieving significant reductions in FVD and camera‑trajectory error on benchmark datasets. The work demonstrates that a geometry‑native latent space can serve as a shared interface between perception and generation models.