3D Texture Alignment Using Depth-Conditioned Estimated Views
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Solution Overview
Problem
Conventional methods for generating textures for 3D models face inefficiencies and resource-intensive data manipulation, leading to inconsistent and low-quality textures, especially under complex geometries and multiple view perspectives, which affect the accuracy and efficiency of texture rendering.
Innovation Solution
An integrated denoising and rendering technique using depth-conditioned text-to-image diffusion models to align textures with 3D geometry, employing a denoiser and renderer to iteratively refine textures based on estimated views and renders, ensuring high fidelity and consistency across various perspectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If separate and disjointed processes are used for generating texture data, then flexibility in processing is maintained, but computational efficiency deteriorates and resource consumption increases
Solution Approach 1:
The patent combines separate texture generation processes (denoising, rendering, optimization) into a single integrated system that processes depth-conditioned text inputs through a unified pipeline, eliminating the need for frequent data manipulation between separate processes while maintaining computational efficiency
2Ease of manufacture
If separate and disjointed processes are used for generating texture data, then process flexibility is maintained, but resource consumption increases
Solution Approach 1:
The integrated system merges multiple texture processing operations into one cohesive workflow, reducing redundant computational operations and resource consumption that would occur if separate processes were executed independently
3Manufacturing precision
If frequent data manipulation and processing are performed, then texture adaptation to complex geometries is improved, but computational cost increases
Solution Approach 1:
The system performs preliminary depth-conditioned processing of text inputs before texture generation, preparing the data in advance to reduce the need for frequent manipulation during the actual texture synthesis process, thereby maintaining precision while reducing computational cost
4Ease of manufacture
If conventional texture generation methods are used, then simplicity of implementation is maintained, but texture quality and consistency deteriorate
Solution Approach 1:
The patent introduces depth-conditioned text representations as an intermediary that bridges simple text inputs and complex texture generation, allowing conventional simple processing interfaces to produce high-quality textures through the mediating depth-conditioned representation
Data Source
AI summary
Various examples, systems, and methods are disclosed relating to texture synthesis. A first computing system determine, using a denoiser and based at least on an input indicating one or more characteristics of a scene, a plurality of estimated views of the scene corresponding to a texture. The first computing system can render, from a model of the texture, a plurality of renders of the texture, at least one render of the plurality of renders being associated with a corresponding estimated view of the plurality of estimated views. The first computing system can update the model of the texture based at least on the plurality of renders and the plurality of estimated views. The first computing system can update the plurality of estimated views based at least on the plurality of renders.