DeepWrinkles: Accurate and Realistic Clothing Modeling (1808.03417v1)

Abstract: We present a novel method to generate accurate and realistic clothing deformation from real data capture. Previous methods for realistic cloth modeling mainly rely on intensive computation of physics-based simulation (with numerous heuristic parameters), while models reconstructed from visual observations typically suffer from lack of geometric details. Here, we propose an original framework consisting of two modules that work jointly to represent global shape deformation as well as surface details with high fidelity. Global shape deformations are recovered from a subspace model learned from 3D data of clothed people in motion, while high frequency details are added to normal maps created using a conditional Generative Adversarial Network whose architecture is designed to enforce realism and temporal consistency. This leads to unprecedented high-quality rendering of clothing deformation sequences, where fine wrinkles from (real) high resolution observations can be recovered. In addition, as the model is learned independently from body shape and pose, the framework is suitable for applications that require retargeting (e.g., body animation). Our experiments show original high quality results with a flexible model. We claim an entirely data-driven approach to realistic cloth wrinkle generation is possible.

• The paper proposes a hybrid framework combining linear shape modeling and cGAN-based wrinkle generation to achieve realistic clothing deformations.
• It leverages real-world 4D scan data to statistically separate body pose effects from garment dynamics, ensuring high fidelity across motion sequences.
• Experiments demonstrate improved visual quality and computational efficiency over traditional physics-based simulations, benefiting AR/VR and digital media.

Analysis of "DeepWrinkles: Accurate and Realistic Clothing Modeling"

The paper "DeepWrinkles: Accurate and Realistic Clothing Modeling" contributes to the domain of computer graphics and virtual garment rendering by presenting a novel framework termed DeepWrinkles. This framework is engineered to produce high-fidelity and realistic clothing deformations from real-world 4D scan data. The authors argue for an entirely data-driven approach, distinguishing their methodology from traditional physics-based simulations, which are recognized for being computationally intensive and reliant on heuristic parameters.

Framework Overview

DeepWrinkles is structured around two complementary modules: a global shape deformation model and a fine wrinkle generation mechanism. The global shape deformations are managed through a linear subspace model, learned from 3D scans of clothed individuals in motion. This statistical approach factors out influences of the human body’s shape and pose, thereby facilitating applications such as body retargeting in virtual animations.

The second module deals with high-frequency details and operates on the principles of conditional Generative Adversarial Networks (cGANs). This segment of the framework enhances normal maps, ensuring both spatial realism and temporal consistency. Notably, this facet of DeepWrinkles succeeds in capturing and rendering fine details, such as intricate cloth wrinkles, which are often neglected in existing methodologies.

Numerical Results and Claims

The authors demonstrate that DeepWrinkles achieves unprecedented rendering quality through their data-driven schema. Experiments elucidate that their approach significantly outperforms traditional simulation methods, both in visual fidelity and computational efficiency. Various configurations are tested, revealing that leveraging registration normal maps with temporal consistency in the cGAN, yields optimal results. The network architecture benefits from skip connections, reminiscent of those used in U-Nets, which preserve structural coherence essential for realistic outputs.

Implications and Future Directions

Practically, the implications of this research influence the domains of AR/VR, virtual try-on applications, and digital content creation in film and gaming. The ability to accurately simulate clothing dynamics in real-time opens up avenues for interactive media and character design, where realism in clothing can enhance user immersion.

Theoretically, the fusion of low-dimensional linear models for coarse deformations with high-dimensional deep networks for fine details presents a hybrid approach that can be extended to other areas of visual computing. The methodology outlined can be adapted to other deformable objects beyond clothing, suggesting a wider applicability of their data-driven paradigm.

Future work could leverage larger and more diverse datasets to enhance the adaptability and generalization capability of the model. Expanding the scanning setup to prevent occlusions and improve detail capture is an opportunity for enhancing the normal maps further. Integrating this framework with real-time rendering engines could push the boundaries of current applications in digital environments.

In summation, DeepWrinkles proposes a highly technical and original method for capturing realistic clothing deformations, innovatively bridging the gap seen in previous physics-based and visual-based modeling approaches. The results presented in this paper make a strong case for the role of deep learning, particularly GANs, in transforming the quality and realism of virtual clothing in computational graphics.