3D Texture Generation Using Multi-View Diffusion Consistency
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Solution Overview
Problem
Existing techniques for generating high-quality texture data for 3D digital objects are inefficient and costly, relying heavily on manual effort by skilled specialists, and often result in poor visual appearance due to visible boundaries and inconsistencies between stitched 2D images.
Innovation Solution
A texture data generation computing system that utilizes a trained neural network to generate multi-view diffusion-generated images, determining cross-frame attention features to improve consistency and calculate texture data values based on pairs of multi-view rendered and diffusion-generated images, reducing the need for manual effort and resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual painting techniques are used to generate high-quality texture data, then the quality and appearance of the texture data is improved, but the time expenditure and resource cost increase significantly
Solution Approach 1:
The patent replaces manual painting techniques (mechanical human effort) with an automated neural network system that processes multi-view images to generate texture data. The neural network automatically synthesizes high-quality textures without requiring human specialists to manually paint each texture map, thereby maintaining texture quality while dramatically reducing time expenditure.
Solution Approach 2:
The system enables self-service texture generation where the neural network automatically processes the multi-view images and generates texture data without requiring manual intervention. The automated pipeline performs texture synthesis, blending, and refinement operations autonomously, eliminating the need for human specialists to perform repetitive manual painting tasks.
2Area of stationary object
If multiple 2D images are stitched together to create texture maps, then the coverage area is improved, but visible boundaries and inconsistencies appear between the stitched images
Solution Approach 1:
The patent changes the fundamental parameter of texture generation from stitching discrete 2D images to synthesizing continuous textures through neural network processing. By transforming the input multi-view images through learned parameters and transformations, the system generates seamless textures that maintain visual consistency across large surface areas without the boundary artifacts inherent in traditional stitching methods.
Solution Approach 2:
The system combines multiple views and diffusion-generated images into a unified texture synthesis process, creating a composite texture map that integrates information from multiple sources seamlessly. The neural network blends the composite input data to produce a unified texture that maintains visual consistency across the entire surface area without visible boundaries between source images.
3Manufacturing precision
If high-quality texture data is generated manually by specialists, then the visual appearance quality is improved, but the productivity and output rate decrease
Solution Approach 1:
The patent replaces manual texture creation by specialists with an automated neural network system that processes multiple views simultaneously. This substitution maintains high visual appearance quality through learned synthesis while dramatically increasing productivity by processing numerous textures in parallel without human intervention, thereby resolving the contradiction between quality and output rate.
Solution Approach 2:
The system performs preliminary diffusion-based image generation and feature extraction before final texture synthesis. By pre-processing the multi-view images through diffusion models to extract relevant features and generate intermediate representations, the system prepares optimized input data that enables faster and higher-quality final texture generation, thereby increasing overall productivity without sacrificing visual appearance quality.
Data Source
AI summary
A texture data generation computing system generates texture data for 3D digital objects based on pairs of multi-view digital images. A rendering engine generates a multi-view rendered image including a set of rendered views depicting a 3D digital object. A diffusion image generation model generates a multi-view diffusion-generated image including a set of diffusion-generated views depicting the 3D digital object with a visual appearance. In addition, the diffusion image generation model determines, for each diffusion-generated view, a respective cross-frame attention feature set describing additional diffusion-generated views. Based on a texture depicted in the set of diffusion-generated views, the texture data generation computing system modifies a texture data object. In some cases, the texture data generation computing system provides the modified texture data object to an additional computing system configured to modify a digital graphical environment based on the texture data object.