Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering
Ruofan Liang, Zan Gojcic, Merlin Nimier-David, David Acuna, Nandita Vijaykumar, Sanja Fidler, Zian Wang
2024-08-20
Summary
This paper presents a method called Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering, which helps insert virtual objects into real-world images in a realistic way.
What's the problem?
When adding virtual objects to photos, it's important to make them look like they belong in the scene, which means matching the lighting, shadows, and materials of the real environment. Current methods often struggle to do this accurately, leading to unrealistic results where the virtual objects don’t fit well with the real background.
What's the solution?
The authors propose using a personalized large diffusion model to guide a process called inverse rendering. This method helps recover the lighting and color settings of the scene, allowing for realistic integration of virtual objects into images or videos. By understanding how light interacts with surfaces, the model can create more convincing shadows and reflections for the inserted objects.
Why it matters?
This research is significant because it enhances the ability to create realistic images that combine real and virtual elements. This technology could be useful in various fields such as gaming, film production, and augmented reality, where blending digital content with real-world visuals is crucial.
Abstract
The correct insertion of virtual objects in images of real-world scenes requires a deep understanding of the scene's lighting, geometry and materials, as well as the image formation process. While recent large-scale diffusion models have shown strong generative and inpainting capabilities, we find that current models do not sufficiently "understand" the scene shown in a single picture to generate consistent lighting effects (shadows, bright reflections, etc.) while preserving the identity and details of the composited object. We propose using a personalized large diffusion model as guidance to a physically based inverse rendering process. Our method recovers scene lighting and tone-mapping parameters, allowing the photorealistic composition of arbitrary virtual objects in single frames or videos of indoor or outdoor scenes. Our physically based pipeline further enables automatic materials and tone-mapping refinement.