VIRGi: View-dependent Instant Recoloring of 3D Gaussians Splats

• URL: http://arxiv.org/abs/2603.02986v1
• Date: Tue, 03 Mar 2026 13:41:17 GMT
• Title: VIRGi: View-dependent Instant Recoloring of 3D Gaussians Splats
• Authors: Alessio Mazzucchelli, Ivan Ojeda-Martin, Fernando Rivas-Manzaneque, Elena Garces, Adrian Penate-Sanchez, Francesc Moreno-Noguer,
• Abstract summary: We introduce VIRGi, a novel approach for rapidly editing the color of scenes modeled by 3DGS.<n>By fine-tuning the weights of a single user, the color edits are seamlessly propagated to the entire scene in just two seconds.<n>An exhaustive validation on diverse datasets demonstrates significant quantitative and qualitative advancements over competitors.
• Score: 53.602701067430075
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D Gaussian Splatting (3DGS) has recently transformed the fields of novel view synthesis and 3D reconstruction due to its ability to accurately model complex 3D scenes and its unprecedented rendering performance. However, a significant challenge persists: the absence of an efficient and photorealistic method for editing the appearance of the scene's content. In this paper we introduce VIRGi, a novel approach for rapidly editing the color of scenes modeled by 3DGS while preserving view-dependent effects such as specular highlights. Key to our method are a novel architecture that separates color into diffuse and view-dependent components, and a multi-view training strategy that integrates image patches from multiple viewpoints. Improving over the conventional single-view batch training, our 3DGS representation provides more accurate reconstruction and serves as a solid representation for the recoloring task. For 3DGS recoloring, we then introduce a rapid scheme requiring only one manually edited image of the scene from the end-user. By fine-tuning the weights of a single MLP, alongside a module for single-shot segmentation of the editable area, the color edits are seamlessly propagated to the entire scene in just two seconds, facilitating real-time interaction and providing control over the strength of the view-dependent effects. An exhaustive validation on diverse datasets demonstrates significant quantitative and qualitative advancements over competitors based on Neural Radiance Fields representations.

Related papers

• Edit3r: Instant 3D Scene Editing from Sparse Unposed Images [40.421700685587346]
  We present Edit3r, a framework that reconstructs and edits 3D scenes in a single pass from unposed, view-inconsistent, instruction-edited images.<n>We show that Edit3r achieves superior semantic alignment and enhanced 3D consistency compared to recent baselines.
  arXiv  Detail & Related papers  (2025-12-31T18:59:53Z)
• Tinker: Diffusion's Gift to 3D--Multi-View Consistent Editing From Sparse Inputs without Per-Scene Optimization [42.00640307135371]
  We introduce Tinker, a versatile framework for high-fidelity 3D editing.<n>Tinker delivers robust, multi-view consistent edits from as few as one or two images.<n>We believe that Tinker represents a key step towards truly scalable, zero-shot 3D editing.
  arXiv  Detail & Related papers  (2025-08-20T16:02:59Z)
• Visibility-Uncertainty-guided 3D Gaussian Inpainting via Scene Conceptional Learning [63.94919846010485]
  3D Gaussian inpainting (3DGI) is challenging in effectively leveraging complementary visual and semantic cues from multiple input views.<n>We propose a method that measures the visibility uncertainties of 3D points across different input views and uses them to guide 3DGI.<n>We build a novel 3DGI framework, VISTA, by integrating VISibility-uncerTainty-guided 3DGI with scene conceptuAl learning.
  arXiv  Detail & Related papers  (2025-04-23T06:21:11Z)
• ExScene: Free-View 3D Scene Reconstruction with Gaussian Splatting from a Single Image [4.366356163044466]
  Existing methods are often limited to reconstruct low-consistency 3D scenes with narrow fields of view from single-view input.<n>We propose ExScene, a two-stage pipeline to reconstruct an immersive 3D scene from any given single-view image.<n>ExScene achieves consistent and immersive scene reconstruction using only single-view input.
  arXiv  Detail & Related papers  (2025-03-31T09:33:22Z)
• Decoupling Appearance Variations with 3D Consistent Features in Gaussian Splatting [50.98884579463359]
  We propose DAVIGS, a method that decouples appearance variations in a plug-and-play manner.<n>By transforming the rendering results at the image level instead of the Gaussian level, our approach can model appearance variations with minimal optimization time and memory overhead.<n>We validate our method on several appearance-variant scenes, and demonstrate that it achieves state-of-the-art rendering quality with minimal training time and memory usage.
  arXiv  Detail & Related papers  (2025-01-18T14:55:58Z)
• WE-GS: An In-the-wild Efficient 3D Gaussian Representation for Unconstrained Photo Collections [8.261637198675151]
  Novel View Synthesis (NVS) from unconstrained photo collections is challenging in computer graphics. We propose an efficient point-based differentiable rendering framework for scene reconstruction from photo collections. Our approach outperforms existing approaches on the rendering quality of novel view and appearance synthesis with high converge and rendering speed.
  arXiv  Detail & Related papers  (2024-06-04T15:17:37Z)
• IReNe: Instant Recoloring of Neural Radiance Fields [54.94866137102324]
  We introduce IReNe, enabling swift, near real-time color editing in NeRF.<n>We leverage a pre-trained NeRF model and a single training image with user-applied color edits.<n>This adjustment allows the model to generate new scene views, accurately representing the color changes from the training image.
  arXiv  Detail & Related papers  (2024-05-30T09:30:28Z)
• Learning Naturally Aggregated Appearance for Efficient 3D Editing [90.57414218888536]
  We learn the color field as an explicit 2D appearance aggregation, also called canonical image.<n>We complement the canonical image with a projection field that maps 3D points onto 2D pixels for texture query.<n>Our approach demonstrates remarkable efficiency by being at least 20 times faster per edit compared to existing NeRF-based editing methods.
  arXiv  Detail & Related papers  (2023-12-11T18:59:31Z)
• PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields [60.66412075837952]
  We present PaletteNeRF, a novel method for appearance editing of neural radiance fields (NeRF) based on 3D color decomposition. Our method decomposes the appearance of each 3D point into a linear combination of palette-based bases. We extend our framework with compressed semantic features for semantic-aware appearance editing.
  arXiv  Detail & Related papers  (2022-12-21T00:20:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.