PanDORA: Casual HDR Radiance Acquisition for Indoor Scenes

Most novel view synthesis methods such as NeRF are unable to capture the true high dynamic range (HDR) radiance of scenes since they are typically trained on photos captured with standard low dynamic range (LDR) cameras. While the traditional exposure bracketing approach which captures several images at different exposures has recently been adapted to the multi-view case, we find such methods to fall short of capturing the full dynamic range of indoor scenes, which includes very bright light sources. In this paper, we present PanDORA: a PANoramic Dual-Observer Radiance Acquisition system for the casual capture of indoor scenes in high dynamic range. Our proposed system comprises two 360{\deg} cameras rigidly attached to a portable tripod. The cameras simultaneously acquire two 360{\deg} videos: one at a regular exposure and the other at a very fast exposure, allowing a user to simply wave the apparatus casually around the scene in a matter of minutes. The resulting images are fed to a NeRF-based algorithm that reconstructs the scene's full high dynamic range. Compared to HDR baselines from previous work, our approach reconstructs the full HDR radiance of indoor scenes without sacrificing the visual quality while retaining the ease of capture from recent NeRF-like approaches.

MyStyle++: A Controllable Personalized Generative Prior

In this paper, we propose an approach to obtain a personalized generative prior with explicit control over a set of attributes. We build upon MyStyle, a recently introduced method, that tunes the weights of a pre-trained StyleGAN face generator on a few images of an individual. This system allows synthesizing, editing, and enhancing images of the target individual with high fidelity to their facial features. However, MyStyle does not demonstrate precise control over the attributes of the generated images. We propose to address this problem through a novel optimization system that organizes the latent space in addition to tuning the generator. Our key contribution is to formulate a loss that arranges the latent codes, corresponding to the input images, along a set of specific directions according to their attributes. We demonstrate that our approach, dubbed MyStyle++, is able to synthesize, edit, and enhance images of an individual with great control over the attributes, while preserving the unique facial characteristics of that individual.

TileGen: Tileable, Controllable Material Generation and Capture

Recent methods (e.g. MaterialGAN) have used unconditional GANs to generate per-pixel material maps, or as a prior to reconstruct materials from input photographs. These models can generate varied random material appearance, but do not have any mechanism to constrain the generated material to a specific category or to control the coarse structure of the generated material, such as the exact brick layout on a brick wall. Furthermore, materials reconstructed from a single input photo commonly have artifacts and are generally not tileable, which limits their use in practical content creation pipelines. We propose TileGen, a generative model for SVBRDFs that is specific to a material category, always tileable, and optionally conditional on a provided input structure pattern. TileGen is a variant of StyleGAN whose architecture is modified to always produce tileable (periodic) material maps. In addition to the standard "style" latent code, TileGen can optionally take a condition image, giving a user direct control over the dominant spatial (and optionally color) features of the material. For example, in brick materials, the user can specify a brick layout and the brick color, or in leather materials, the locations of wrinkles and folds. Our inverse rendering approach can find a material perceptually matching a single target photograph by optimization. This reconstruction can also be conditional on a user-provided pattern. The resulting materials are tileable, can be larger than the target image, and are editable by varying the condition.