Recent advancements in diffusion models have introduced fast sampling methods that can effectively produce high-quality images in just one or a few denoising steps. Interestingly, when these are distilled from existing diffusion models, they often maintain alignment with the original model, retaining similar outputs for similar prompts and seeds. These properties present opportunities to leverage fast sampling methods as a shortcut-mechanism, using them to create a preview of denoised outputs through which we can backpropagate image-space losses.

Large-scale text-to-image models enable a wide range of image editing techniques, using text prompts or even spatial controls. However, applying these editing methods to multi-view images depicting a single scene leads to 3D-inconsistent results. In this work, we focus on spatial control-based geometric manipulations and introduce a method to consolidate the editing process across various views.

The joint reconstruction of shape and appearance for translucent objects from real-world data poses a challenge in computer graphics, especially when dealing with complex layered materials like leaves or paper. The traditional assumption of diffuse transmittance falls short, and more accurate Monte-Carlo-based models are often needed to reproduce their appearance. To accurately capture the translucent appearance, an acquisition system needs to be carefully designed.