← 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
3d ago

WilLaGS: Latent-Conditional 3D Appearance Fields for Robust Gaussian Splatting In-the-Wild

WilLaGS introduces a unified framework that enhances 3D Gaussian Splatting for in-the-wild scenes by learning a continuous global appearance manifold with a β‑VAE and generating dynamic Tri‑Plane features for spatially‑varying local illumination. It also incorporates a self‑supervised perceptual masking mechanism using a Teacher‑Student EMA architecture to suppress transient artifacts and identify inconsistent regions. Experiments on multiple datasets show that WilLaGS achieves state‑of‑the‑art reconstruction quality and novel view synthesis while preserving real‑time rendering efficiency.

By Yuhao Bai, Qianqiu Tan, Lilong Chen, Huanhuan Lv, Lijun Chen
ragbenchmarks
More like this →