Reflecting Reality: Enabling Diffusion Models to Produce Faithful Mirror Reflections (2409.14677v1)

Abstract: We tackle the problem of generating highly realistic and plausible mirror reflections using diffusion-based generative models. We formulate this problem as an image inpainting task, allowing for more user control over the placement of mirrors during the generation process. To enable this, we create SynMirror, a large-scale dataset of diverse synthetic scenes with objects placed in front of mirrors. SynMirror contains around 198K samples rendered from 66K unique 3D objects, along with their associated depth maps, normal maps and instance-wise segmentation masks, to capture relevant geometric properties of the scene. Using this dataset, we propose a novel depth-conditioned inpainting method called MirrorFusion, which generates high-quality geometrically consistent and photo-realistic mirror reflections given an input image and a mask depicting the mirror region. MirrorFusion outperforms state-of-the-art methods on SynMirror, as demonstrated by extensive quantitative and qualitative analysis. To the best of our knowledge, we are the first to successfully tackle the challenging problem of generating controlled and faithful mirror reflections of an object in a scene using diffusion based models. SynMirror and MirrorFusion open up new avenues for image editing and augmented reality applications for practitioners and researchers alike.

• The paper presents MirrorFusion, a novel depth-conditioned diffusion model that accurately generates mirror reflections from complex 3D scene data.
• It leverages the comprehensive SynMirror dataset, which integrates color images, depth maps, normal maps, and segmentation masks to enhance reflection fidelity.
• Experimental results demonstrate improved image quality with a PSNR of 24.22 in unmasked areas and an IoU of 0.567, highlighting strong geometric consistency.

Enabling Diffusion Models to Produce Faithful Mirror Reflections

The paper "Reflecting Reality: Enabling Diffusion Models to Produce Faithful Mirror Reflections" presents significant advancements in the domain of generative models, focusing on the generation of realistic mirror reflections using diffusion-based models. The authors introduce an innovative approach known as MirrorFusion, which addresses the complex problem of generating geometrically consistent and photo-realistic mirror reflections. This essay provides a comprehensive summary of the paper, discussing its methodology, results, and implications.

Introduction

Generating realistic mirror reflections remains a notable challenge in computer vision and image synthesis. Diffusion-based generative models, though successful in many applications, struggle with replicating intricate geometric cues such as mirror reflections. The authors tackle this by leveraging an image inpainting framework, enriched with depth-conditioning, to produce controlled and faithful reflections.

SynMirror Dataset

A significant aspect of this research is the introduction of SynMirror, a large-scale dataset comprising synthetic scenes specifically designed for mirror reflection tasks. SynMirror includes approximately 198,204 samples rendered from 66,068 unique 3D objects. It provides color images, depth maps, normal maps, and instance-wise segmentation masks, enabling comprehensive geometric properties capture. This dataset supersedes current datasets which lack the necessary scale and complexity for training generative models on mirror reflections.

Methodology

The core proposition of the paper is MirrorFusion, a depth-conditioned inpainting model. MirrorFusion employs a dual-branch architecture inspired by BrushNet. The conditional U-Net in this framework is enhanced to process depth information alongside image and mask inputs, facilitating the generation of reflections that align with the 3D structure of scenes. This approach allows the model to utilize geometric cues, critically improving the accuracy of reflections.

Depth Normalization and Conditioning

To generate precise reflections, the authors highlight the importance of depth normalization. The depth values are normalized using a tailored affine-invariant approach, ensuring compatibility with monocular depth estimation methods and improving ref lection fidelity. The integration of depth maps with the conditional U-Net allows for maintaining object geometry and ensuring the reflections are consistent with the scene's spatial configuration.

Results

The authors performed extensive quantitative and qualitative evaluations on MirrorBench, a benchmark subset of SynMirror. Metrics such as PSNR, SSIM, LPIPS, and IoU were used to assess the performance of MirrorFusion against state-of-the-art inpainting methods, both zero-shot and fine-tuned.

• Image Quality: MirrorFusion achieved a PSNR of 24.22 on unmasked regions, outperforming BrushNet-FT's score of 23.06.
• Reflection Quality: The model also led in masked regions with a PSNR of 20.35, demonstrating superior reflection accuracy.
• Geometric Consistency: An IoU score of 0.567 further reflects the model's ability to generate spatially accurate reflections.

Implications and Future Work

The methodology and results introduced by this research have substantial implications for various applications, including image editing, augmented reality, and visual effects in media production. By formulating the problem as an inpainting task and utilizing depth-conditioning, the authors open new avenues for generating consistent and controllable reflections in synthetic imagery.

Future research can expand on this work by exploring:

• Refinement of the SynMirror dataset with more diverse and complex scenes.
• Integration of real-world depth estimation techniques with enhanced accuracy.
• Adoption of the MirrorFusion framework in real-time applications, such as augmented reality.

Conclusion

This paper demonstrates a significant leap forward in the capability of generative models to produce realistic mirror reflections. The introduction of the SynMirror dataset and the innovative MirrorFusion model underscores the importance of considering geometric information in generating high-fidelity reflections. This work sets a solid foundation for future advancements in controlled image synthesis and presents exciting possibilities for both theoretical exploration and practical applications.