Point-Based Clothing Modeling for Flexible Virtual Try-On
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Solution Overview
Problem
Existing methods struggle to model realistic clothing on humans due to wide variations in geometry and appearance, particularly in interaction with human bodies, and lack flexibility in topology and appearance modeling.
Innovation Solution
A method using point clouds and neural networks to model clothing geometry and appearance, employing a draping network trained on video sequences to adapt outfits to various body poses and shapes, with neural rendering for appearance capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predefined outfit templates of fixed topology are used to model clothing geometry, then the modeling process becomes simpler and faster, but the ability to represent diverse clothing variations and topological changes is limited
Solution Approach 1:
The patent transforms the fixed topology constraint into a variable parameter system by representing clothing surfaces as point clouds with learnable deformation fields. The neural network learns continuous transformations that adapt the base template to match diverse clothing topologies and geometries, allowing the system to handle variable topologies through parameter learning rather than fixed structural constraints
Solution Approach 2:
The system introduces dynamic adaptation by using neural networks to learn pose-dependent and style-dependent deformations. The clothing model dynamically adjusts its geometry and topology based on the input pose parameters and style codes, enabling real-time adaptation to different body configurations and clothing styles without requiring predefined templates for each scenario
2Reliability
If physics-based simulation is used to model clothing deformation, then realistic interaction between clothing and human body is achieved, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent replaces the mechanical physics-based simulation system with a neural network-based deformation field. Instead of computing physical forces, masses, and material properties in real-time, the system learns clothing deformation patterns from training data and applies them through neural network inference, dramatically reducing computational complexity while maintaining visual realism
Solution Approach 2:
The system performs preliminary learning during the training phase where physics-based simulations or annotated data are used to teach the neural network realistic clothing behaviors. Once trained, the network encodes this knowledge and can generate realistic clothing deformations without requiring actual physics computation during inference, achieving speedup while preserving realism
3Productivity
If image-to-image transfer methods are used for virtual try-on, then the process is simplified and runs faster, but the 3D geometry and realistic appearance capture are lost
Solution Approach 1:
The patent transitions from 2D image-to-image transfer to 3D point cloud-based modeling by representing clothing as three-dimensional point clouds with associated style codes. This dimensional elevation allows the system to capture and manipulate 3D geometry while still leveraging neural network efficiency, enabling both accurate geometric representation and realistic appearance transfer
Data Source
AI summary
Provided are virtual try-on applications, telepresence applications, relating to modeling realistic clothing worn by humans and realistic modeling of humans in three-dimension (3D). Proposed is a hardware comprising software products that perform method for imaging clothes on a person, that is adapted to the body pose and the body shape, based on point cloud draping model, the method including using of point cloud and a neural network that synthesizes such point clouds to capture/model the geometry of clothing outfits, and using of point based differentiable neural rendering to capture the appearance of clothing outfits.