Diffusion-Based Cloth Registration for Wrinkle-Accurate Modeling
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Solution Overview
Problem
Current methods for registering clothing with large deformations, such as wrinkles, in virtual environments face challenges due to reliance on texture, which is unsuitable for textureless regions and requires hyperparameter tuning, making it difficult to accurately model complex clothing deformations.
Innovation Solution
The use of a diffusion-based shape prior learned from pre-captured clothing data to guide the registration process, employing a multi-stage guidance sampling process that stabilizes registrations by denoising and refining deformations, allowing for accurate registration even without texture information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If texture-based registration methods are used for clothing, then registration accuracy can be improved in textured regions, but it becomes unsuitable for textureless regions and requires hyperparameter tuning
Solution Approach 1:
The patent introduces a diffusion model as an intermediary that learns the underlying shape prior of clothing from training data. This diffusion model acts as a mediator between the input scan and the registration process, enabling accurate registration in textureless regions by providing learned shape constraints rather than relying on texture information.
Solution Approach 2:
The patent performs preliminary training of the diffusion model on a large dataset of clothing scans before actual registration. This preliminary action captures the statistical properties and shape variations of clothing, which are then utilized during the registration phase to guide the deformation field estimation, eliminating the need for hyperparameter tuning during runtime.
2Device complexity
If traditional registration methods are used for clothing with large deformations, then the process can be simpler, but accuracy in modeling complex clothing deformations deteriorates
Solution Approach 1:
The patent replaces traditional mechanics-based or optimization-based registration methods with a data-driven diffusion model approach. Instead of relying on complex optimization procedures and hand-crafted energy functions, the system uses a pre-trained diffusion model that has learned the deformation patterns, achieving high accuracy with a more straightforward application process.
3Measurement precision
If diffusion-based shape prior is used to guide registration, then registration accuracy with large deformations is improved, but computational complexity increases
Solution Approach 1:
The computationally intensive diffusion model training is performed in advance during an offline phase. The trained model parameters are then stored and reused during the actual registration process, which only requires forward passes through the pre-trained network. This shifts the computational burden from the runtime registration to the preliminary training phase.
Solution Approach 2:
The patent creates a learned representation (the diffusion model) that copies the essential deformation characteristics of clothing from training data. This copied knowledge is then applied to new registration tasks without requiring re-computation of the underlying physical principles, achieving both accuracy and efficiency.
Data Source
AI summary
A method and system for cloth registration to improve modeling clothes by providing, for example, wrinkle-accurate cloth registration. The method includes obtaining an input scan of clothing in motion. The method includes generating a mesh representing the cloth in the scan based on a diffusion-based shape prior. The method includes registering a model of the cloth from the scan using a guidance process including at least: guiding deformation of the clothing based on a coarse registration signal based on the mesh and guiding the deformation of the clothing based on a distance between points in the mesh and a template mesh.