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Conference Paper
Robust In-Engine Texture Optimization from Inconsistent Generative Targets
- Issue Date
- 2026-07
- Citation
- ACM SIGGRAPH 2026, pp.1-12
- Publisher
- ACM
- Language
- English
- Type
- Conference Paper
- Abstract
- 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.
- KSP Keywords
- Film production, Finite difference, Ghosting artifacts, Gradient estimation, High-fidelity, Optimization Framework, Uncertainty map, inverse rendering, robust formulation, visual quality
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
