Generative Image Rendering With Editable Light and Material Maps
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Solution Overview
Problem
Generative models face challenges in preserving identity and consistency of objects across frames, providing precise control over attributes like lighting and material properties, and efficiently simulating realistic light interactions, limiting their ability to replicate classic graphics rendering workflows.
Innovation Solution
Introduce editable light and material controls into generative models, integrating diffusion-based renderers that use material maps, lighting maps, and noise vectors to condition the denoising process, allowing for precise control and realistic rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative models are used for image rendering, then creative flexibility and style transfer are improved, but control over lighting and material properties deteriorates
Solution Approach 1:
The rendering process is segmented into independent controllable components: text prompts control semantic content, material maps control surface properties, and lighting maps control illumination. This segmentation allows each aspect to be adjusted independently while maintaining overall coherence, resolving the contradiction between creative flexibility and precise control.
Solution Approach 2:
Material maps and lighting maps serve as intermediary representations that bridge the gap between high-level text prompts and low-level pixel generation. These intermediate structures enable precise control over specific attributes (materials, lighting) without sacrificing the generative model's creative capabilities, as users can edit these maps directly or through natural language.
2Manufacturing precision
If generative models attempt to simulate realistic light interactions, then visual realism is improved, but computing resource consumption increases
Solution Approach 1:
Lighting information is pre-computed and stored in lighting maps before the rendering process. Material properties are pre-defined in material maps. This preliminary preparation allows the generative model to directly utilize these pre-processed representations during rendering, avoiding the need to compute complex light interactions from scratch and significantly reducing computational resources while maintaining visual realism.
3Adaptability or versatility
If generative models are used for inverse rendering, then ability to deduce scene properties from images is improved, but precision in recovering material maps deteriorates
Solution Approach 1:
The system employs feedback mechanisms where the generated material maps and lighting maps are used to re-render the scene, and the result is compared with the original input image. This feedback loop allows iterative refinement of the recovered material properties, improving precision by adjusting the material maps until the re-rendered image closely matches the input, thus resolving the precision issue in inverse rendering.
Data Source
AI summary
Embodiments of the present disclosure relate to rendering and inverse rendering using one or more generative models. “Rendering” refers to the process of generating a final visual image, video frame, or animation from a 2D or 3D model. “Inverse rendering” is a process that involves deducing or estimating the properties (e.g., material maps or other properties such as geometry, lighting, and textures) of a scene from observed images or visual data. Essentially, it aims to reverse the traditional rendering process. Various aspects of the present disclosure introduce editable light and material controls into generative models to allow for artistic creation. Various embodiments integrate generative models as a renderer for classic rendering pipelines to upcycle and enhance the style of rendered content.