Digital Image Decaling via Predicted Surface Geometry
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Solution Overview
Problem
Conventional digital image decaling techniques fail to produce realistic results for complex or flexible object geometries, such as garments, as they ignore local geometry and rely on manual annotations or generic UV maps that often do not match the specific object, leading to unrealistic and time-consuming processes.
Innovation Solution
The use of machine learning to predict the surface geometry of objects in digital images by generating a surface map that aligns decal overlays with the predicted geometry, allowing for automatic and computationally efficient decaling without user intervention, using neural networks to map pixel locations to 3D object models and adjust decal placement based on surface features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional decaling techniques overlay images without accounting for local geometry, then the process is simple and fast, but the result looks unrealistic for complex or flexible object geometries
Solution Approach 1:
The patent introduces surface maps as an intermediary data structure that bridges the gap between simple overlay operations and complex geometry. The surface map encodes geometric information about the object's surface, allowing the decaling process to account for local geometry without requiring complex manual annotations. This intermediary enables automatic adaptation of decal placement and transformation based on the underlying surface characteristics.
Solution Approach 2:
The patent transforms the decaling approach by changing parameters from fixed overlay coordinates to dynamic parameters derived from surface maps. Instead of using static UV coordinates, the system uses surface map parameters that adapt to the object's geometry, allowing the decal to conform to complex surfaces automatically. This parameter transformation enables the system to handle flexible geometries like garments without manual intervention.
2Manufacturing precision
If manual annotations or generic UV maps are used to account for object geometry, then the decaling result can be more accurate, but the process becomes time-consuming and requires user intervention
Solution Approach 1:
The system performs self-service by automatically generating surface maps from the input image without requiring manual annotations. The surface map generation process is performed computationally automatically, extracting geometric features directly from the image data. This eliminates the need for time-consuming manual UV mapping while still achieving accurate decal placement that adapts to the object's geometry.
Solution Approach 2:
The patent performs preliminary action by pre-computing surface maps that encode geometric information before the actual decaling operation. These surface maps are generated in advance and stored, allowing subsequent decaling operations to proceed quickly without repeated manual intervention. The preliminary computation of surface geometry enables fast, automated decaling while maintaining high accuracy.
3Productivity
If generic UV maps are used for decaling, then the process is computationally efficient, but the UV map often does not match the specific object, leading to unrealistic results
Solution Approach 1:
The patent applies local quality by generating surface maps that capture the specific geometric characteristics of each object instance rather than using generic UV maps. The surface map is computed locally from the input image, adapting to the specific object's geometry, wrinkles, and features. This local adaptation ensures that the decal matches the specific object while maintaining computational efficiency through automated generation.
Data Source
AI summary
Decal application techniques as implemented by a computing device are described to perform decaling of a digital image. In one example, learned features of a digital image using machine learning are used by a computing device as a basis to predict the surface geometry of an object in the digital image. Once the surface geometry of the object is predicted, machine learning techniques are then used by the computing device to configure an overlay object to be applied onto the digital image according to the predicted surface geometry of the overlaid object.