Neural Gaffer: Relighting Any Object via Diffusion
(2406.07520)Abstract
Single-image relighting is a challenging task that involves reasoning about the complex interplay between geometry, materials, and lighting. Many prior methods either support only specific categories of images, such as portraits, or require special capture conditions, like using a flashlight. Alternatively, some methods explicitly decompose a scene into intrinsic components, such as normals and BRDFs, which can be inaccurate or under-expressive. In this work, we propose a novel end-to-end 2D relighting diffusion model, called Neural Gaffer, that takes a single image of any object and can synthesize an accurate, high-quality relit image under any novel environmental lighting condition, simply by conditioning an image generator on a target environment map, without an explicit scene decomposition. Our method builds on a pre-trained diffusion model, and fine-tunes it on a synthetic relighting dataset, revealing and harnessing the inherent understanding of lighting present in the diffusion model. We evaluate our model on both synthetic and in-the-wild Internet imagery and demonstrate its advantages in terms of generalization and accuracy. Moreover, by combining with other generative methods, our model enables many downstream 2D tasks, such as text-based relighting and object insertion. Our model can also operate as a strong relighting prior for 3D tasks, such as relighting a radiance field.
Overview
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The paper introduces Neural Gaffer, a diffusion model-based approach to single-image relighting that does not rely on scene decomposition.
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Neural Gaffer is trained on a synthetic relighting dataset (RelitObjaverse) and integrates environment map rotation and HDR representations to generate high-quality relit images.
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The model demonstrates versatility in text-based and 3D relighting tasks and shows superior performance compared to existing frameworks based on quantitative and qualitative analyses.
Overview of the Neural Gaffer: Relighting Any Object via Diffusion
The paper presents a novel approach to the challenging task of single-image relighting by introducing an end-to-end 2D relighting diffusion model named Neural Gaffer. The approach leverages diffusion models, which have recently emerged as powerful tools in visual content generation, to generate high-quality relit images without requiring explicit scene decomposition. This paper outlines the underlying methodology, the creation of a comprehensive synthetic dataset, the model architecture, and the practical applications and limitations of the proposed approach.
Methodology
Neural Gaffer builds on a pre-trained diffusion model that is fine-tuned on a purpose-built synthetic dataset to enhance its understanding of lighting conditions. The model accepts any single object image and synthesizes an accurate relit output under novel environmental lighting conditions specified by an HDR environment map. Key innovations include:
- Synthetic Relighting Dataset: The dataset, called RelitObjaverse, is constructed by filtering high-quality 3D models from Objaverse and rendering them under a wide variety of lighting conditions to capture the interplay of geometry, materials, and illumination.
- Lighting-Conditioned Diffusion Model: The diffusion model architecture integrates two critical design choices:
- Rotating the environment map to align with the camera's coordinate frame before input, improving the model's ability to interpret lighting directions.
- Employing both LDR and normalized HDR representations of the environment map to ensure that the full energy spectrum is captured without losing lighting detail.
Training and Fine-Tuning: The model undergoes fine-tuning to effectively incorporate lighting variations and achieve realistic relighting results. This involves encoding the input image and the processed environment map into latents that condition the denoising process of the diffusion model.
Applications and Performance
Neural Gaffer demonstrates its versatility and utility in several downstream tasks beyond single-image relighting:
- 2D Task Enablement: The diffusion model can be utilized for text-based relighting, where an environment map generated from a text description can be used to relight images. It also facilitates object insertion tasks by integrating relighting details to match an object's appearance with the target background environment.
- 3D Relighting: The model acts as a robust prior for 3D tasks, contributing to a two-stage pipeline for relighting 3D radiance fields. This includes:
- A coarse relighting stage that adjusts the object's appearance under the new lighting.
- A detail refinement stage using a diffusion guidance loss to achieve high-fidelity results.
Quantitative and Qualitative Analysis
Quantitative assessments using PSNR, SSIM, and LPIPS metrics on synthetic validation datasets reveal Neural Gaffer's superior ability to generalize relighting across diverse objects and scenarios compared to recent relighting frameworks such as DiLightNet. Qualitative evaluations on real-world images further exhibit the model's consistent performance under varying lighting conditions, maintaining high visual fidelity and accurate highlights/shadows.
Implications and Future Developments
The proposed approach has significant theoretical and practical implications. By incorporating powerful diffusion models, Neural Gaffer enhances the relighting capabilities for a wide range of objects, facilitating integration into various industries such as filmmaking, photorealistic simulations, and augmented reality (AR). Future developments in this domain could include enhancing the resolution capabilities, extending the model's applicability to more specific domains like portrait relighting, and improving real-time relighting performance.
Conclusion
Neural Gaffer represents a substantial advancement in single-image relighting methodologies by leveraging diffusion models and synthetic datasets to achieve high-accuracy and generalizable results. The approach's robustness and versatility open up numerous practical applications, potentially setting a new standard in image relighting tasks. While there remain challenges and areas for refinement, the foundational contributions of this work pave the way for more sophisticated and comprehensive relighting solutions in the future.
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