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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Geometry image super-resolution with AnisoCBConvNet architecture for efficient cloth modeling.

Jong-Hyun Kim¹, Sun-Jeong Kim², Jung Lee²

¹School of Software Application, Kangnam University, Yongin, Gyeonggi, Republic of Korea.

|August 24, 2022

Summary

This summary is machine-generated.

We developed AnisoCBConvNet, a novel neural network that enhances low-resolution cloth meshes into high-quality, detailed results without oscillations. This method improves CG VFX for games and movies by enabling stable, efficient high-resolution cloth simulation.

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Area of Science:

Computer Graphics
Artificial Intelligence
Computational Physics

Background:

Cloth simulation is crucial for realistic computer graphics (CG) and visual effects (VFX).
Existing methods for high-resolution cloth simulation can be computationally expensive and prone to oscillations or noise.
Generating high-quality meshes from low-resolution data presents a significant challenge.

Purpose of the Study:

To propose a novel convolutional neural network, Anisotropic Constrained-Boundary Convolutional Neural Network (AnisoCBConvNet), for stable super-resolution of cloth meshes.
To improve the quality and detail of cloth meshes generated from low-resolution simulation data.
To alleviate surface wrinkling and noise issues common in existing geometry image-based approaches.

Main Methods:

Developed AnisoCBConvNet, applying super-resolution to low-resolution cloth meshes.
Trained the network using pairs of low-resolution (LR) and high-resolution (HR) cloth simulation data, converted to 2D geometry images.
Employed anisotropic weight control near boundaries to prevent oscillations and surface wrinkling.

Main Results:

AnisoCBConvNet successfully converts LR geometry images to HR geometry images, producing detailed cloth meshes.
The method effectively alleviates surface wrinkling and noise, outperforming existing geometry image approaches.
Stable and efficient performance was confirmed across various cloth simulation scenarios.

Conclusions:

AnisoCBConvNet provides a stable and efficient solution for generating high-quality cloth meshes via super-resolution.
The proposed method enhances the realism of CG VFX in games and movies by improving cloth simulation fidelity.
This approach offers a significant advancement in creating detailed and artifact-free cloth dynamics.