5 accepted papers
We introduce Intrinsic Image Fusion, a method that reconstructs high-quality physically based materials from multi-view images.Material reconstruction is highly underconstrained and typically relies on analysis-by-synthesis, which requires expensive and noisy path tracing. To better constrain the op
We introduce IntrinsiX, a novel method that generates high-quality intrinsic images from text description. In contrast to existing text-to-image models whose outputs contain baked-in scene lighting, our approach predicts physically-based rendering (PBR) maps. This enables the generated outputs to be…
We present Intrinsic Image Diffusion a generative model for appearance decomposition of indoor scenes. Given a single input view we sample multiple possible material explanations represented as albedo roughness and metallic maps. Appearance decomposition poses a considerable challenge in computer vi…
We introduce LightIt a method for explicit illumination control for image generation. Recent generative methods lack lighting control which is crucial to numerous artistic aspects of image generation such as setting the overall mood or cinematic appearance. To overcome these limitations we propose t…
Convolutional neural networks were the standard for solving many computer vision tasks until recently, when Transformers of MLP-based architectures have started to show competitive performance. These architectures typically have a vast number of weights and need to be trained on massive datasets; he…