An Empty Room is All We Want: Automatic Defurnishing of Indoor Panoramas (2405.03682v1)

Abstract: We propose a pipeline that leverages Stable Diffusion to improve inpainting results in the context of defurnishing -- the removal of furniture items from indoor panorama images. Specifically, we illustrate how increased context, domain-specific model fine-tuning, and improved image blending can produce high-fidelity inpaints that are geometrically plausible without needing to rely on room layout estimation. We demonstrate qualitative and quantitative improvements over other furniture removal techniques.

• The paper presents an enhanced defurnishing method that leverages domain-specific model tuning and Stable Diffusion to remove furniture from indoor panoramas.
• The paper demonstrates a novel technique for processing wide-context equirectangular images, enabling coherent inpainting without relying on room layout predictions.
• The paper employs tailored blending methods to seamlessly integrate inpainted regions, offering superior visual realism for applications in real estate and interior design.

Inpainting for Indoor Panorama Defurnishing: An Enhanced Approach with Stable Diffusion

Introduction to Enhanced Inpainting

The paper discusses a method for "defurnishing" indoor panoramas using a pipeline built upon Stable Diffusion, a popular generative AI model. Defurnishing refers to digitally removing items, like furniture, from images to provide a clean slate which could be useful for interior design, real estate showcasing, or virtual staging. The authors employ a refined approach that promises better removal with minimal hallucinations (or unintended items appearing in the cleaned-up image) without relying on room layout predictions.

Key Improvements and Techniques

The proposed method hinges on three primary advancements to combat common challenges in panorama inpainting:

1. Domain-Specific Model Tuning:
   • The model is fine-tuned specifically for defurnishing with a robust training dataset comprising unfurnished panoramas. This focuses on adequately handling empty spaces, shadows, and reflections which are crucial for realistic defurnishing without object hallucinations.
2. Handling Wide Context:
   • Instead of working with traditional perspective views, this method directly works with equirectangular panorama images. It's beneficial because such images encompass a wider context, helping the AI understand the larger spatial layout which is essential for generating coherent results after removing objects.
3. Improved Blending Techniques:
   • Once objects are removed and the underlying space is inpainted, the method applies a tailored blending approach. This ensures that the new (inpaint) regions seamlessly integrate with the untouched parts of the image, maintaining a natural and consistent appearance across the panorama.

Practical Implications and Theoretical Significance

Practical Implications:

• Real Estate: Sellers and agents can create clean visualizations of properties without physical modifications, helping potential buyers envision the space with their preferences.
• Interior Design: Professionals can more easily propose redesigns without the distraction of existing furnishings in a space.

Theoretical Significance:

• Demonstrates the intricate handling and understanding of indoor panoramic imaging in AI, pushing the boundaries of what's possible with neural networks in visual content manipulation.
• Shows potential pathways to reduce reliance on geometric estimations (like room layout algorithms) which can be complex and error-prone, by training the model on specific tasks (like defurnishing).

Future Directions and Research Opportunities

While the system excels in managing and processing isolated images, further development could target consistency across multiple views or entire digital twins. Integrating components that consider how multiple panoramas relate might offer a more uniform and realistic defurnishing on larger scales. Additionally, exploring this technology's adaptability to different types of environments (like offices or outdoor areas) could extend its utility.

Considering scalability, training models on higher-resolution images or experimenting with different forms of neural network architectures could enhance detail retention and reduce the need for subsequent processing steps.

Challenges and Limitations

• Resolution and Detail: While the method excels in general object removal, finer details and textures can be challenging, sometimes leading to less-than-ideal clarity or realism in the cleaned areas.
• Occasional Hallucinations: Despite improvements, there are instances where the model may still introduce artifacts or misinterpret shadows as objects needing filling.

Conclusion

This paper marks a notable step in the use of AI for realistic and functional imagery modifications, particularly in interior spaces. By focusing on specific challenges and applying tailored solutions, the research paves the way for more nuanced and practical applications of AI in digital imagery and real estate visualization. With ongoing improvements and expanded training, methods like these could soon become standard tools in various industries reliant on visual media.