Target-Augmented Material Maps for Realistic Tileable Textures
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Solution Overview
Problem
Editing two-dimensional texture maps in graphics design software requires significant artistic expertise and often results in unrealistic or non-tileable materials, especially when transitioning from procedural to realistic textures.
Innovation Solution
Utilizing a pre-trained generative adversarial network (GAN) to encode and optimize material appearances from input maps, guided by target images, to produce a projected latent vector that minimizes statistical differences and maintains visual plausibility, ensuring tileability and realism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual editing of texture maps is performed by users, then artistic control and customization are improved, but the requirement for artistic expertise increases and realism may deteriorate
Solution Approach 1:
A GAN-based intermediary system is introduced between the user and the texture map editing process. The system takes user inputs and automatically generates realistic material appearances through learned transformations, eliminating the need for users to possess artistic expertise while maintaining high realism through the GAN's trained understanding of material properties
Solution Approach 2:
The manual mechanical editing process is replaced with an automated computational system based on GANs. Instead of users manually adjusting texture parameters, the system uses machine learning models to automatically transform procedural textures into realistic materials based on target image guidance, substituting human artistic skill with algorithmic intelligence
2Ease of manufacture
If procedural textures are used, then ease of generation is improved, but visual realism and photographic quality deteriorate
Solution Approach 1:
The system transforms the parameters of procedural textures by learning the mapping between procedural parameters and photographic material properties. The GAN adjusts multiple texture parameters simultaneously (color, roughness, normal maps, etc.) to transition from procedural to photorealistic appearance while maintaining the underlying procedural structure for tileability
Solution Approach 2:
The system creates composite material representations by combining procedural texture generation with photographic target guidance. The output material map integrates both procedural advantages (tileability, parametric control) and photographic realism (target image appearance), resulting in a hybrid material that exhibits properties of both approaches
3Manufacturing precision
If target image guidance is applied, then visual plausibility and realism are improved, but computational complexity and processing time increase
Solution Approach 1:
The GAN is pre-trained on extensive datasets of real materials and procedural textures before deployment. This preliminary training phase captures the complex relationships between procedural parameters and photorealistic appearances, allowing the system to perform rapid transformations during actual use without requiring complex real-time computations
Solution Approach 2:
The system employs feedback mechanisms through the GAN's loss functions that continuously compare generated materials against target images and adjust parameters accordingly. The feedback loop guides the optimization process to maintain visual plausibility while converging on realistic material appearances that match the target guidance
Data Source
AI summary
Certain aspects and features of this disclosure relate to rendering images using target-augmented material maps. In one example, a graphics imaging application is loaded with a scene and an input material map, as well as a file for a target image. A stored, material generation prior is accessed by the graphics imaging application. This prior, as an example, is based on a pre-trained, generative adversarial network (GAN). An input material appearance from the input material map is encoded to produce a projected latent vector. The value for the projected latent vector is optimized to produce the material map that is used to render the scene, producing a material map augmented by a realistic target material appearance.