Cage-based Texture Transfer with Geometric Filtering

📅 2026-06-23
📈 Citations: 0
Influential: 0
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🤖 AI Summary
Existing real-time texture transfer methods struggle to balance speed and artifact suppression, often exhibiting noticeable artifacts or relying on computationally intensive large models. This work proposes a cage-based geometric filtering approach that automatically identifies non-decorative zones (NCZs) to effectively suppress artifacts without requiring large-scale training or manual annotations. Integrated with a lightweight texture transfer algorithm, the method is deployable on consumer-grade hardware, substantially reducing computational and memory overhead. Experiments demonstrate real-time performance of approximately 70 ms on mobile devices for meshes with around 4.8k faces, achieving a favorable trade-off among efficiency, robustness, and visual quality.
📝 Abstract
Real-time texture transfer expands the creative horizon for interactive applications, enabling seamless detail projection in scenarios that range from digital character cosmetics to procedural automotive texturing. Yet, its practical application is governed by inherent trade-offs between processing speed and suppression of artifacts. Low-latency transfer methods frequently fail to suppress artifacts, and robust alternatives rely on large-scale models that are costly in training and memory. Our proposed method bridges the gap between efficiency and robustness by using a cage-based geometric filtering method to identify Non-Cosmetic Zones (NCZs) for artifact suppression. While other models are resource-intensive and require multiple days of training on manually annotated datasets, we are able to successfully suppress artifacts and achieve immediate deployment on consumer-grade hardware. Our framework achieved highly efficient runtimes of ~70ms on mobile devices for a ~4.8k triangle mesh.
Problem

Research questions and friction points this paper is trying to address.

texture transfer
artifact suppression
real-time rendering
computational efficiency
geometric filtering
Innovation

Methods, ideas, or system contributions that make the work stand out.

cage-based
geometric filtering
texture transfer
artifact suppression
real-time
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