DIGITAL IMAGE DECALING

Smart overview of the Invention

The patent application describes a method for applying digital decals to images using advanced computing techniques. Traditional methods often fail to realistically overlay patterns or textures onto complex objects due to their inability to account for the intricate geometries of the objects depicted. This new approach leverages machine learning to predict and adapt to the surface geometry of objects, allowing for more realistic and efficient decaling.

Challenges with Conventional Techniques

Conventional decaling techniques typically ignore the local geometry of objects, which can lead to unrealistic results, especially with complex shapes like garments. These older methods either rely on generic UV maps that rarely match the specific object in an image or require manual adjustments by experts, which are time-consuming and inconsistent. The new technique addresses these shortcomings by using machine learning to automatically predict surface geometries, ensuring a better fit for the decal.

Machine Learning Approach

The described method uses a neural network to extract features from digital images, which helps in predicting the object's surface geometry. This prediction is crucial for mapping decals accurately onto the object. The machine learning model automatically generates a surface map (e.g., UV map) that aligns the decal with the object's geometry in a digital image, thus improving realism and computational efficiency.

Decal Application Process

Once the surface geometry is predicted, the system maps the decal onto the digital image according to this geometry. This involves aligning pixel locations on the object with positions on a 3D model template, effectively "wrapping" the decal texture around the object. The process ensures that decals conform to features like wrinkles or curves, maintaining realism without manual intervention.

Training the Neural Network

To train the neural network, digital images are annotated with anchor points that correspond to positions on a 3D model template. This training data helps the system learn how different parts of an image relate to a 3D surface. The method allows for efficient and accurate generation of surface maps across numerous images, enhancing the ability of computing devices to apply realistic decals automatically.