Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering (2408.09702v1)

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.

• The paper introduces DiPIR, integrating a physically based renderer with diffusion models for photorealistic object insertion.
• It employs a lightweight personalization scheme and an advanced score distillation sampling loss to optimize lighting and tone-mapping.
• Experimental results on Waymo and PolyHaven datasets demonstrate significant improvements in perceptual realism and rendering consistency.

Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering

The paper "Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering" by Ruofan Liang et al. presents a novel method for inserting virtual objects into real-world scenes in a photorealistic manner. This paper makes significant strides in addressing the ill-posed problem of inverse rendering by leveraging large diffusion models (DMs) to guide the estimation of scene lighting and tone-mapping.

Summary of Contributions

The proposed method, Diffusion Prior for Inverse Rendering (DiPIR), builds on three primary contributions:

1. Physically Based Renderer Integration: DiPIR integrates a physically based renderer to simulate the interactions between light and a 3D asset, ensuring accurate generation of the final composited image. This involves accounting for unknown tone-mapping curves to mimic the camera sensor response, enabling realistic rendering of virtual objects.
2. Personalized Diffusion Model: The method introduces a lightweight personalization scheme for pre-trained diffusion models, based on the input image and the type of inserted asset. This personalization ensures that the diffusion model adapts to the specifics of the scene, improving the consistency and realism of the inserted objects.
3. Advanced Score Distillation Sampling (SDS) Loss: The paper proposes a variant of the SDS loss that improves training stability and leverages the personalized diffusion model. This allows the diffusion model to act similarly to a human evaluator, providing valuable feedback signals for optimizing the rendering process.

Methodology

Virtual Scene and Light Representation

To insert a virtual object into a real-world scene, the paper employs a 3D proxy geometry, enabling the correct placement and interaction of the virtual object with the background. The scene's lighting is represented using Spherical Gaussian (SG) parameters, allowing for differentiation and optimization.

Differentiable Rendering

The rendering process is differentiable, allowing for gradient-based optimization of the lighting and tone-mapping parameters. This involves:

• Foreground Image Rendering: Path tracing is used to render the foreground image of the inserted object based on the estimated lighting.
• Shadow Ratio Calculation: The shadow ratio, which accounts for the effect of the inserted object on the background, is computed as the ratio between the radiance received by the proxy geometry with and without the object.
• Tone-Mapping Adjustment: An optimizable tone correction function adjusts the tone-mapping to match the camera sensor response, improving the visual consistency of the inserted object.

Diffusion Model Personalization

The paper addresses the limitations of off-the-shelf diffusion models by fine-tuning them with scene-specific data. This personalization ensures that the diffusion model understands the scene's lighting and geometry, improving the quality of object insertion. By generating additional synthetic images for the insertable class concept (e.g., cars), the method avoids overfitting and ensures robust guidance for the insertion task.

Experimental Results

The method's efficacy is demonstrated through extensive experiments on benchmark datasets, including:

• Waymo Dataset: The user paper results show that DiPIR outperforms state-of-the-art lighting estimation methods, achieving higher perceptual realism in diverse outdoor street scenes.
• PolyHaven Dataset: DiPIR consistently produces more photorealistic insertions than baselines, validated through quantitative metrics and user preference scores.

Implications and Future Directions

The research has significant practical and theoretical implications:

• Practical Applications: DiPIR can be used in virtual production, interactive gaming, and synthetic data generation, enhancing the realism of virtual object insertion in various domains.
• Theoretical Insights: The integration of diffusion models with inverse rendering processes provides a novel approach to addressing the ill-posed nature of lighting estimation. The use of a physically based renderer combined with DM guidance offers a robust framework for future research in photorealistic rendering.
• Future Developments: Future work could explore more complex lighting representations, address the limitations of SG-based lighting for highly specular materials, and extend the rendering formulation to incorporate effects like reflections from the scene itself.

Conclusion

The paper provides a meticulously detailed and technically sound method for photorealistic object insertion, leveraging the strengths of diffusion models for scene-specific guidance. The integration with a physically based renderer and the proposed advanced SDS loss mark significant advancements in the field of inverse rendering, offering a robust solution for realistic virtual object insertion in dynamic scenes. The proposed method's potential applications in virtual production and augmented reality highlight its practical relevance and pave the way for further exploration and enhancement in related domains.