Robust In-Engine Texture Optimization from Inconsistent Generative Targets

摘要

We propose a robust texture optimization framework that handles inconsistent AI-generated targets by operating directly within non-differentiable production pipelines. Standard optimization approaches struggle with inconsistent targets, often producing blurry textures and ghosting artifacts. Moreover, existing inverse rendering methods rely on differentiable renderers, causing a rendering gap when assets are deployed in production engines. To address the inconsistencies, we introduce a robust formulation that jointly optimizes a deformation grid and an uncertainty map. This formulation effectively decouples geometric misalignment from semantic hallucinations. To avoid the rendering gap, we leverage finite-difference gradient estimation to operate entirely within standard rasterizers. Extensive experiments demonstrate that our approach recovers sharp, high-fidelity textures from inconsistent AI-generated targets, achieving higher visual quality than existing methods. This makes our technique a practical tool for film production, gaming, and virtual reality, where flexibility and visual quality are paramount.