Material Transforms from Disentangled NeRF Representations
This work addresses material transformation for computer graphics applications, offering a novel method but is incremental as it builds on existing disentangled NeRF representations.
The paper tackles the problem of transferring material transformations across scenes by learning to map BRDFs from pairs of scenes in varying conditions, such as dry and wet, using disentangled NeRF representations, and shows it can apply these transformations to unseen scenes with similar materials, validated through experiments on synthetic and real-world datasets.
In this paper, we first propose a novel method for transferring material transformations across different scenes. Building on disentangled Neural Radiance Field (NeRF) representations, our approach learns to map Bidirectional Reflectance Distribution Functions (BRDF) from pairs of scenes observed in varying conditions, such as dry and wet. The learned transformations can then be applied to unseen scenes with similar materials, therefore effectively rendering the transformation learned with an arbitrary level of intensity. Extensive experiments on synthetic scenes and real-world objects validate the effectiveness of our approach, showing that it can learn various transformations such as wetness, painting, coating, etc. Our results highlight not only the versatility of our method but also its potential for practical applications in computer graphics. We publish our method implementation, along with our synthetic/real datasets on https://github.com/astra-vision/BRDFTransform