arXiv:2611.06070v1 Announce Type: new Abstract: While single-image 3D texture generation has advanced significantly, conditioning texture synthesis on multi-view reference images remains largely unde...
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.
arXiv:2604. 05182v2 Announce Type: replace-cross Abstract: We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows affects feed-forward 3D reconstruction.
DirectUV is an image-conditioned UV texture diffusion framework that generates high-quality textures directly in UV space using a pretrained image VAE and a Diffusion Transformer. It introduces Surface-Aware Positional Encoding (SAPE), which replaces standard 2D-grid positional encoding with encodings derived from 3D surface coordinates, enabling attention to operate based on surface proximity rather than UV-grid distance. A multi-level extension assigns different attention heads to progressively finer subdivisions of the UV patch, allowing the model to reason about surface structure at multiple granularities, resulting in sharper and more globally consistent textures, especially in occluded and view-unseen regions.
arXiv:2610.20610v1 Announce Type: new Abstract: Generating novel views from limited observations requires preserving both the appearance and spatial structure of a scene. Although recent approaches e...
Generating novel views from limited observations requires preserving both the appearance and spatial structure of a scene. Although recent approaches exploit fine-grained geometric cues, they do not e...
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...
FlexPainter is a new texture generation pipeline that offers flexible multi‑modal conditional guidance and produces highly consistent textures across multiple views. It builds a shared conditional embedding space to aggregate different input modalities and introduces an image‑based CFG method to separate structural and style information for reference image‑based stylization. The system generates multi‑view images simultaneously using a grid representation, applies view synchronization and adaptive weighting during diffusion sampling for local consistency, and finally uses a 3D‑aware texture completion model with a texture enhancement model to produce seamless, high‑resolution texture maps, outperforming state‑of‑the‑art methods in flexibility and quality.
Anchor3DGS introduces a pose‑free, feed‑forward 3D Gaussian Splatting framework that replaces pixel‑aligned Gaussian prediction with a compact anchor‑based representation. By placing 3D anchors guided by information entropy, the method concentrates primitives in visually complex regions while ensuring broad spatial coverage, and each anchor aggregates multi‑view features to generate local Gaussians. A lightweight feature enhancement module refines the coarse RGB output, resulting in state‑of‑the‑art novel view synthesis quality with an order of magnitude fewer Gaussian primitives than prior approaches.
DiDE is a training‑free framework that enables independent control of color and texture when stylizing 3D assets generated by rectified flow‑based image‑to‑3D models. It leverages the overcomplete latent space of these models, partitioning channels so that texture occupies a small subset while color is encoded in a free subspace. Experiments on the newly collected Disen3D‑Bench benchmark demonstrate that DiDE outperforms existing 2D and 3D stylization methods in color fidelity, texture transfer, and content preservation.
The paper introduces MatLat, a material latent space designed for generating high‑quality physically‑based rendering (PBR) textures on 3D meshes. It fine‑tunes a pretrained VAE to incorporate new material channels while preserving latent distribution, and adds a locality‑preserving regularization that aligns cropped latent patches with image regions. Experiments and ablation studies show that these components together improve PBR texture fidelity and outperform prior baselines.
Recent 3D foundation models can generate high-quality assets from a single image, but degrade markedly on unconstrained multi-image inputs, often producing distorted geometry, over-smoothed textures, and chaotic colors. We argue that this failure stems not from limited model capacity, but from a mismatch between single-image cross-attention and the multi-image setting: existing models lack a principled way to decide which image each 3D voxel should trust at each denoising step.