Enterprise AI Research Analysis
2D Gaussian Splatting with Semantic Alignment for Image Inpainting
Authors: Hongyu Li¹, Chaofeng Chen², Xiaoming Li³, Guangming Lu¹
Affiliations: ¹Harbin Institute of Technology, Shenzhen, ²School of Artificial Intelligence, Wuhan University, ³Nanyang Technological University
Publication: github.com/hitlhy715/2DGS-inpaint
This paper introduces the first image inpainting framework based on 2D Gaussian Splatting (2DGS). It leverages the continuous rendering paradigm of 2DGS to ensure pixel-level coherence and incorporates DINO features for global semantic consistency. A patch-wise rasterization strategy is used for efficiency. Experiments show competitive performance in quantitative metrics and perceptual quality, establishing a new direction for applying Gaussian Splatting to 2D image processing.
Unlocking Next-Gen Image Restoration with 2D Gaussian Splatting
Achieving unparalleled coherence and efficiency in image inpainting.
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Avg. Inference Time (ms)
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Avg. Perceptual Quality (LPIPS↓)
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Avg. Image Fidelity (FID↓)
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2DGS Framework for Inpainting
Deep Analysis & Enterprise Applications
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Addressing Core Inpainting Challenges
Continuous Pixel Generation for CoherenceTraditional image inpainting methods, relying on discrete pixel synthesis via CNNs or Transformers, often struggle to reconstruct coherent pixel-level structures, especially in complex textures. Our 2DGS framework inherently promotes smooth and continuous pixel generation.
Pioneering 2DGS Application
2D Gaussian Splatting First for Image InpaintingThis research introduces the very first framework to leverage 2D Gaussian Splatting for high-quality image inpainting. It directly encodes incomplete images into a continuous Gaussian feature space, allowing effective reconstruction of missing regions without explicit optimization of scene-wide parameters.
Enterprise Process Flow: 2DGS Inpainting Pipeline
Masked Input Image (Xm)
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U-Net Encoder + DINO Guidance
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Gaussian Parameters (μ, L, c, σ)
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Patch-level Rasterization
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Complete Output Image (X)
Scalable High-Resolution Processing with Patch-level Rasterization
Challenge: High computational cost and GPU memory for high-resolution images with many Gaussians.
Solution: We introduce a patch-wise rasterization strategy. This divides images into manageable segments, processing them independently and blending overlapping regions to maintain spatial continuity.
Impact: Significantly reduces GPU memory consumption and accelerates rendering, making high-resolution inpainting efficient and scalable without sacrificing quality. This is crucial for enterprise applications handling large image datasets.
Semantic Consistency for Global Coherence
DINO Features Robust Global Semantic GuidanceTo address the challenge of maintaining global semantic consistency across independently processed patches, we integrate features from a pretrained DINO model. DINO features are remarkably robust to small masks and can be effectively adapted for large masks, ensuring inpainted content remains contextually consistent with the surrounding scene.
| Method | LPIPS (Small CelebA-HQ)↓ | LPIPS (Large Places2)↓ | FID (Large Places2)↓ | Inference Speed (ms)↓ |
|---|---|---|---|---|
| Ours | 0.028 | 0.094 | 5.03 | 32.52 |
| LaMa | 0.037 | 0.104 | 3.60 | 15.80 |
| RePaint | 0.066 | 0.117 | 20.62 | 79035.84 |
| Latent-Code | 0.098 | 0.131 | 5.31 | 45.67 |
Our method demonstrates competitive performance against state-of-the-art baselines on CelebA-HQ and Places2 datasets, for both small and large mask scenarios. Lower values are better for FID and LPIPS. While LaMa is notably faster in inference and achieves better FID on Places2 large masks, our method excels in perceptual quality (LPIPS) across various scenarios, indicating superior visual coherence. Our inference speed is significantly better than diffusion-based methods like RePaint.
Superior Qualitative Results Across Diverse Datasets
Challenge: Generating visually plausible and semantically consistent content for complex images and large missing regions.
Solution: Our 2DGS framework, combined with DINO-based semantic guidance, produces high-quality visual outputs.
Impact: Qualitative comparisons on CelebA-HQ (faces), Places2 (natural scenes), FFHQ (portraits), and ImageNet-100 (object-centric) demonstrate that our method consistently generates coherent, artifact-free, and contextually appropriate completions, even for challenging scenarios with complex textures and fine details (Figures 4, 8-11).
Establishing a New Frontier
Breakthrough 2D Gaussian Splatting for Image ProcessingThis work is pivotal in establishing 2D Gaussian Splatting as a powerful and efficient representation for general 2D image processing tasks, particularly for image restoration and broader visual synthesis. It opens new research avenues beyond its initial success in 3D.
Roadmap: Enhanced Controllability & Generative AI Integration
Current Limitation: The framework operates without external user-guided controls (e.g., text prompts, structural cues).
Future Direction: Integrating cross-modal conditioning mechanisms will enable more flexible and user-guided generation, allowing users to influence inpainting results with semantic instructions.
Impact: This enhancement will significantly broaden the applicability of 2DGS in interactive image editing and creative generative AI workflows, moving towards more versatile and user-responsive systems.
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Phased Implementation Roadmap
Our structured approach ensures seamless integration and rapid value realization for your enterprise.
Phase 1: Discovery & Strategy
Assess current workflows, identify key integration points for 2DGS, and define AI-driven image processing objectives. Establish core metrics for success.
Phase 2: Pilot & Customization
Deploy a pilot program with custom 2DGS models tailored to your specific image datasets. Integrate DINO-based semantic guidance and patch-level processing for optimal performance.
Phase 3: Integration & Scaling
Seamlessly integrate the enhanced inpainting solution into your existing production systems. Scale capabilities to handle high-volume, high-resolution image restoration tasks across your enterprise.
Phase 4: Optimization & Futureproofing
Continuous monitoring and refinement of AI models. Explore advanced features like cross-modal conditioning for user-guided inpainting and broader visual synthesis applications.
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