SGRA: semantically-guided region-adaptive local texture editing in 3D Gaussian splatting editing

doi:10.11996/JG.j.2095-302X.2026051072

Abstract

Abstract:

Local editing of 3D scenes constitutes a fundamental task in content generation, with substantial application value in domains such as post-production, digital twins, and game development. 3D Gaussian Splatting (3DGS), as a representative technique, has achieved pivotal breakthroughs in real-time rendering, thereby providing robust support for efficient editing workflows. Nevertheless, existing diffusion-based methods for local texture editing of 3D Gaussian scenes are commonly plagued by semantic leakage and background drift, which limit editing fidelity. To tackle these challenges, a semantically guided region-adaptive approach was proposed for local texture editing of 3D scenes. The proposed method was designed to deliver high-quality texture editing while rigorously preserving the geometric and appearance consistency of non-edited regions. Concretely, a high-confidence 3D semantic mask generation module was first introduced, which incorporates a vision-language foundation model, employs LangSAM in conjunction with a confidence-filtering strategy, and constructs a robust 3D semantic field via multi-view voting, thereby eliminating semantic leakage at its source. Subsequently, a saliency-guided anchor view propagation module was devised, which automatically selects optimal anchor views based on semantic saliency scores and integrates a multi-view attention consistency mechanism to effectively mitigate temporal flickering. Finally, a region-adaptive gradient update strategy was formulated, which decouples the foreground editing loss from the background anchoring loss, enabling fine-grained scene optimization. Experiments conducted on five editing tasks across the Instruct-NeRF2NeRF and Mip-NeRF 360 datasets demonstrated that, when compared with baseline methods such as GaussianEditor and DGE, the proposed method performed better overal on quantitative metrics such as CLIP Score, Background LPIPS, Background PSNR, and Temporal LPIPS. Qualitative evaluation further showed that the proposed method not only exhibited overall advantages in complex semantic decoupling and background preservation tasks, but also achieved a favorable balance between texture quality and geometric integrity.

Key words: 3D Gaussian splatting, 3D scene editing, diffusion models, semantic guidance, region- adaptation

CLC Number:

TP 391

YU Bing, JIN Zhenzhen, DI Youxian, SUN Haorui, DING Youdong, HUANG Dongjin. SGRA: semantically-guided region-adaptive local texture editing in 3D Gaussian splatting editing[J]. Journal of Graphics, 2026, 47(5): 1072-1083.

Figures/Tables 8

References 33

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场景任务	方法	CLIP Score ↑	Background LPIPS ↓	Background PSNR↑	Temporal LPIPS ↓
Person (Pineapple)	GaussianEditor	0.234 9	0.133 5	14.40	0.093 4
	DGE	0.246 6	0.469 7	13.83	0.096 3
	本文	0.261 5	0.193 3	15.15	0.063 4
Person (Jacket)	GaussianEditor	0.260 2	0.429 1	11.49	0.031 3
	DGE	0.221 3	0.441 3	11.19	0.029 1
	本文	0.222 0	0.390 7	12.62	0.030 2
Face (Clown)	GaussianEditor	0.245 8	0.396 2	14.76	0.198 3
	DGE	0.243 1	0.413 1	13.39	0.185 6
	本文	0.251 7	0.383 1	15.45	0.191 4
Bear (Brown)	GaussianEditor	0.253 3	0.456 6	11.99	0.166 5
	DGE	0.258 1	0.525 5	11.55	0.161 4
	本文	0.263 3	0.450 8	12.14	0.160 6
Bicycle (Blue)	GaussianEditor	0.250 5	0.523 7	14.72	0.170 3
	DGE	0.140 6	0.534 6	15.27	0.195 8
	本文	0.248 9	0.496 8	17.67	0.124 8

场景任务	方法	CLIP Score ↑	Background LPIPS ↓	Background PSNR↑	Temporal LPIPS ↓
Person (Pineapple)	GaussianEditor	0.234 9	0.133 5	14.40	0.093 4
	DGE	0.246 6	0.469 7	13.83	0.096 3
	本文	0.261 5	0.193 3	15.15	0.063 4
Person (Jacket)	GaussianEditor	0.260 2	0.429 1	11.49	0.031 3
	DGE	0.221 3	0.441 3	11.19	0.029 1
	本文	0.222 0	0.390 7	12.62	0.030 2
Face (Clown)	GaussianEditor	0.245 8	0.396 2	14.76	0.198 3
	DGE	0.243 1	0.413 1	13.39	0.185 6
	本文	0.251 7	0.383 1	15.45	0.191 4
Bear (Brown)	GaussianEditor	0.253 3	0.456 6	11.99	0.166 5
	DGE	0.258 1	0.525 5	11.55	0.161 4
	本文	0.263 3	0.450 8	12.14	0.160 6
Bicycle (Blue)	GaussianEditor	0.250 5	0.523 7	14.72	0.170 3
	DGE	0.140 6	0.534 6	15.27	0.195 8
	本文	0.248 9	0.496 8	17.67	0.124 8

Method	Person(Pineapple)			Bear(Brown)
Method	CLIP Score↑	Background LPIPS↓	Temporal LPIPS↓	CLIP Score↑	Background LPIPS↓	Temporal LPIPS↓
w/o Background Anchor	0.264 7	0.315 2	0.095 6	0.269 8	0.642 1	0.185 6
w/o Multi-View Attention	0.268 0	0.312 1	0.098 5	0.275 9	0.560 1	0.241 2
w/o Geometry Lock	0.278 8	0.313 3	0.097 1	0.281 2	0.698 7	0.189 7
Full Method (Ours)	0.261 5	0.193 3	0.063 4	0.263 3	0.450 8	0.160 6

Method	Person(Pineapple)			Bear(Brown)
Method	CLIP Score↑	Background LPIPS↓	Temporal LPIPS↓	CLIP Score↑	Background LPIPS↓	Temporal LPIPS↓
w/o Background Anchor	0.264 7	0.315 2	0.095 6	0.269 8	0.642 1	0.185 6
w/o Multi-View Attention	0.268 0	0.312 1	0.098 5	0.275 9	0.560 1	0.241 2
w/o Geometry Lock	0.278 8	0.313 3	0.097 1	0.281 2	0.698 7	0.189 7
Full Method (Ours)	0.261 5	0.193 3	0.063 4	0.263 3	0.450 8	0.160 6