Generative Detail Enhancement for Physically Based Materials

We present a tool for enhancing the detail of physically based materials using an off-the-shelf diffusion model and inverse rendering. Our goal is to enhance the visual fidelity of materials with detail that is often tedious to author, by adding signs of wear, aging, weathering, etc. As these appearance details are often rooted in real-world processes, we leverage a generative image model trained on a large dataset of natural images with corresponding visuals in context. Starting with a given geometry, UV mapping, and basic appearance, we render multiple views of the object. We use these views, together with an appearance-defining text prompt, to condition a diffusion model. The details it generates are then backpropagated from the enhanced images to the material parameters via inverse differentiable rendering. For inverse rendering to be successful, the generated appearance has to be consistent across all the images. We propose two priors to address the multi-view consistency of the diffusion model. First, we ensure that the initial noise that seeds the diffusion process is itself consistent across views by integrating it from a view-independent UV space. Second, we enforce geometric consistency by biasing the attention mechanism via a projective constraint so that pixels attend strongly to their corresponding pixel locations in other views. Our approach does not require any training or finetuning of the diffusion model, is agnostic of the material model used, and the enhanced material properties, i.e., 2D PBR textures, can be further edited by artists.

A Radiance Field Loss for Fast and Simple Emissive Surface Reconstruction

We present a fast and simple technique to convert images into an emissive surface-based scene representation. Building on existing emissive volume reconstruction algorithms, we introduce a subtle yet impactful modification of the loss function requiring changes to only a few lines of code: instead of integrating the radiance field along rays and supervising the resulting images, we project the training images into the scene to directly supervise the spatio-directional radiance field. The primary outcome of this change is the complete removal of alpha blending and ray marching from the image formation model, instead moving these steps into the loss computation. In addition to promoting convergence to surfaces, this formulation assigns explicit semantic meaning to 2D subsets of the radiance field, turning them into well-defined emissive surfaces. We finally extract a level set from this representation, which results in a high-quality emissive surface model. Our method retains much of the speed and quality of the baseline algorithm. For instance, a suitably modified variant of Instant~NGP maintains comparable computational efficiency, while achieving an average PSNR that is only 0.1 dB lower. Most importantly, our method generates explicit surfaces in place of an exponential volume, doing so with a level of simplicity not seen in prior work.