← Back to all news
arXiv Computer Vision By Zhongliang Liu, Wenjie Liu, Yang Li

DReSG: Diffusion Residuals for Stylized Gaussian Splatting

Read the original on arXiv Computer Vision →

DReSG introduces a 3D-grounded residual-feedback framework for stylizing scenes represented by 3D Gaussian Splatting. It uses attention-guided diffusion proposals as residual targets relative to current renders and progressively integrates these residuals into a shared Gaussian scene via multi-view feedback. The method stabilizes and controls the feedback by modulating residual strength, selecting coverage-aware views, and filtering color updates, achieving competitive stylization while preserving scene structure and cross-view stability.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Computer Vision
Sep 18

ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylization

ArtNVG is a 3D stylization framework that uses reference style images to produce stylized 3D scenes with high reconstruction quality. Built on 3D Gaussian Splatting, it introduces Content‑Style Separated Control and Attention‑based Neighboring‑View Alignment to maintain local color and texture consistency across views. Experiments show that ArtNVG outperforms existing methods in content preservation, style alignment, and local consistency.

By Zixiao Gu, Mengtian Li, Ruhua Chen, Zhongxia Ji, Sichen Guo, Zhenye Zhang, Guangnan Ye, Zuo Hu
safety
More like this →
arXiv AI
Sep 28

Spackle: Completing Large View Single Image NVS with Adaptive Gaussians

Spackle is a lightweight residual learning framework designed to improve large-view single-image novel view synthesis (NVS) by mitigating capacity competition in hybrid decoupled systems that combine 3D Gaussian Splatting (3DGS) and diffusion models. It operates in three stages: predicting base 3DGS attributes, automatically identifying poorly reconstructed regions, and learning a residual 3DGS focused on those areas. During inference, Spackle merges the baseline and augmented Gaussians to produce high-fidelity novel views, achieving state‑of‑the‑art performance on large-view-deviation cases.

By Xuanzhi Liu, Yuhe Zhou, Xinyi Wu, Zhenyao Wu, Jinghao Chen, Ruize Han, Song Wang
diffusionbenchmarks
More like this →