LumiNet: Latent Intrinsics Meets Diffusion Models for Indoor Scene Relighting (2412.00177v2)

Abstract: We introduce LumiNet, a novel architecture that leverages generative models and latent intrinsic representations for effective lighting transfer. Given a source image and a target lighting image, LumiNet synthesizes a relit version of the source scene that captures the target's lighting. Our approach makes two key contributions: a data curation strategy from the StyleGAN-based relighting model for our training, and a modified diffusion-based ControlNet that processes both latent intrinsic properties from the source image and latent extrinsic properties from the target image. We further improve lighting transfer through a learned adaptor (MLP) that injects the target's latent extrinsic properties via cross-attention and fine-tuning. Unlike traditional ControlNet, which generates images with conditional maps from a single scene, LumiNet processes latent representations from two different images - preserving geometry and albedo from the source while transferring lighting characteristics from the target. Experiments demonstrate that our method successfully transfers complex lighting phenomena including specular highlights and indirect illumination across scenes with varying spatial layouts and materials, outperforming existing approaches on challenging indoor scenes using only images as input.

• The paper introduces LumiNet, which disentangles intrinsic scene properties and fuses them with extrinsic lighting through a novel diffusion-based approach.
• It employs a learned MLP adaptor with cross-attention to inject lighting features, capturing detailed effects like specular highlights and indirect illumination.
• Empirical results demonstrate over 20% performance improvement on multiple datasets, highlighting robust generalization across diverse indoor scenes.

Indoor Scene Relighting with LumiNet: Technical Exploration and Implications

The paper under discussion introduces LumiNet, a novel neural architecture designed to address the intricate task of indoor scene relighting by merging latent intrinsic representations with diffusion models. This method seeks to overcome previous challenges in transferring lighting conditions between distinct indoor scenes—a task critical in fields like cinematography, architectural visualization, and augmented reality.

Core Methodology

LumiNet distinguishes itself by employing an innovative approach that centers on disentangling and manipulating latent intrinsic codes derived from images. Unlike traditional methods relying on explicit volumetric rendering or multi-view setups, LumiNet takes advantage of pre-trained models to extract a scene's intrinsic properties (e.g., geometry and albedo) and combines these with the extrinsic lighting features of a target scene. The paper's authors present a modified diffusion architecture, akin to ControlNet, strategically tailored to handle this dual-input structure effectively.

A notable component is a learned Multilayer Perceptron (MLP) adaptor which injects latent extrinsic lighting properties into the generative process via cross-attention mechanisms. This allows for highly detailed relighting that captures both direct and indirect lighting phenomena such as specular highlights and inter-reflections, delivering scenes that maintain the spatial arrangement and material characteristics of the source scene.

Empirical Evaluation

LumiNet undergoes rigorous evaluation on datasets such as the MIT Multi-Illumination dataset, achieving a substantial improvement of over 20% on various quantitative metrics compared to state-of-the-art methods. This impressive performance is accomplished using a relatively small yet diverse training set curated via generative techniques rooted in variational StyleGAN architectures. The use of real-world datasets like Multi-Illumination Images in the Wild and BigTime complements this setup, offering a broad spectrum of lighting conditions during training.

Technical Contributions

The primary contributions of this work can be articulated as follows:

1. Latent Space Exploration: The integration of StyleGAN's intrinsic capabilities and the latent diffusion model allows for a new frontier in lighting transfer, where the intrinsic and extrinsic properties can be separated and manipulated with a high degree of flexibility.
2. Data Curation Strategy: The use of a variational StyleGAN to generate training pairs supplements real-world data, addressing the scarcity of paired lighting data. This approach ensures a rich training paradigm that captures a wide range of lighting interactions.
3. Real-World Applicability: Despite being trained predominantly on same-scene pairs, LumiNet generalizes effectively across entirely different scenes, highlighting the robustness of its learned representations.
4. Relighting Performance: Comprehensive experiments demonstrate that LumiNet exceeds the capabilities of existing methods, especially in handling scenes with varied materials and layout complexities.

Implications and Future Directions

The theoretical implications of LumiNet's approach mark a shift from relying purely on physical modeling techniques towards leveraging learned representations and generative priors. This can inspire future research to explore latent space manipulations for similar tasks like geometric restoration or material editing. Practically, LumiNet opens doors for real-time relighting applications in interactive media and virtual reality, offering an avenue to create more immersive user experiences.

Future work could extend LumiNet's capabilities to handle dynamic environments or improve computational efficiency through optimization at both the architecture and algorithmic levels. Moreover, expanding the architecture's adaptability to creative lighting designs, particularly those that defy typical physical constraints, would further enhance its appeal in diverse applications.

In conclusion, LumiNet sets a formidable precedent by addressing key hurdles in scene relighting with elegant model design and a comprehensive evaluation strategy, thereby strengthening the methodology arsenal available to researchers and practitioners in computer graphics and rendering domains.