Pi-Light Achieves Realistic Full-Image Relighting, Bridging the Synthetic-To-Real Gap

Full-image relighting presents a significant hurdle in computer vision, largely due to the need for extensive paired datasets, maintaining physical accuracy, and achieving broad applicability. Zhexin Liang, Zhaoxi Chen, and Yongwei Chen, all from S-Lab at Nanyang Technological University, alongside Tianyi Wei, Tengfei Wang, and Xingang Pan, address these issues with their new framework, PI-Light. This research introduces a two-stage system leveraging physics-inspired techniques to improve consistency, enforce realistic light transport, and enhance generalisation to real-world images. By combining batch-aware attention, a physics-guided neural rendering module, and carefully designed loss functions, PI-Light not only facilitates efficient finetuning of pretrained models but also establishes a robust benchmark for future work in full-scene relighting, demonstrably synthesising more convincing specular highlights and reflections than existing methods.

Physics-inspired diffusion for realistic image relighting offers compelling

Scientists have demonstrated a breakthrough in full-image relighting, addressing longstanding challenges in data acquisition, physical plausibility, and generalizability. The team achieved this by introducing Physics-Inspired diffusion for full-image reLighting, or PI-Light, a novel two-stage framework leveraging physics-inspired Diffusion models. This research tackles the difficulty of creating realistic relighting effects across entire images, a problem hampered by the lack of large, well-structured datasets and the tendency of existing methods to produce physically implausible results. The study unveils a system capable of synthesizing specular highlights and diffuse reflections with superior performance compared to previous approaches, particularly when applied to real-world scenes.

PI-Light incorporates batch-aware attention, a technique that enhances the consistency of intrinsic predictions across multiple images, improving the overall coherence of the relighting process. The work opens new avenues for controllable and realistic image editing, moving beyond the limitations of purely data-driven methods. This carefully constructed dataset supports supervised learning and enables comprehensive benchmarking of downstream performance. The combination of these innovations results in a system that not only achieves competitive performance but also exhibits strong generalization capabilities, even with limited training data and without relying on fully realistic datasets.

Batch-aware attention for physics-guided image relighting enables consistent

Scientists developed Physics-Inspired for full-image reLight (PI-Light), a two-stage framework addressing challenges in full-image relighting, namely limited paired data, maintaining physical plausibility, and generalizability. The study pioneered batch-aware attention, integrated into both inverse neural rendering and neural forward rendering stages, extending standard self-attention layers to enable global communication across batches. This design significantly improves efficiency and consistency in predicting intrinsic image properties, allowing the model to better understand scene characteristics. Researchers harnessed this approach to facilitate more accurate and coherent relighting across multiple images within a scene.

Experiments demonstrate that this physics-inspired loss enhances generalizability to real-world image editing scenarios, surpassing the performance of prior methods. Furthermore, the work builds upon prior physically-based rendering (PBR) approaches, adopting a two-stage framework of inverse neural rendering followed by neural forward rendering. However, PI-Light distinguishes itself through the implementation of batch-aware attention and the physics-inspired loss function, achieving superior performance and generalization compared to existing models like IC-Light, which often exhibit albedo inconsistencies and imprecise lighting control as demonstrated in comparative figures.

PI-Light synthesises realistic reflections and highlights on surfaces

Scientists have developed Physics-Inspired for full-image reLighting, or PI-Light, a novel two-stage framework leveraging physics-inspired diffusion models to address challenges in full-image relighting. The research tackles difficulties in collecting large-scale paired data, maintaining physical plausibility, and achieving generalizability beyond training distributions. The team incorporated batch-aware attention, improving the consistency of intrinsic predictions across image collections. This attention mechanism enables communication across batches during both inverse neural rendering and neural forward rendering stages, enhancing efficiency and consistency of predicted intrinsic properties.

Results demonstrate the system’s ability to accurately estimate normal and material properties from input images, subsequently predicting shading and generating relit images with precise lighting control. Specifically, the work addresses limitations observed in prior methods, such as alterations in carpet colours and inaccuracies in light source direction, as demonstrated in comparative analyses against IC-Light. This breakthrough delivers improved performance and generalisation to real-world scenes, even with fewer training samples than previous works.

PI-Light achieves realistic physically based relighting in real

Scientists have developed a novel neural rendering approach, termed PI-Light, for full-image relighting, addressing challenges inherent in existing methods. Experiments demonstrate PI-Light’s ability to synthesize specular highlights and reflections across various materials, achieving superior generalization to real-world scenes when compared to previous approaches. The effectiveness of diffuse and physical-based shading losses was confirmed through evaluation on the object50 test dataset, both significantly enhancing model performance and correcting highlight and shadow directions. The authors acknowledge limitations inherent in the complexity of full-image relighting and the need for continued refinement of physics-inspired priors.

This research establishes a robust method for full-image relighting, offering improved physical accuracy and generalizability compared to existing techniques. The curated dataset contributes a valuable benchmark for future work in this area, while the incorporation of physics-inspired principles suggests a promising direction for enhancing the realism and controllability of image editing. Future research could explore extending the framework to handle more complex scenes and lighting conditions, potentially incorporating dynamic lighting effects or advanced material properties.