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TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style (2003.04583v2)

Published 10 Mar 2020 in cs.CV and cs.GR

Abstract: In this paper, we present TailorNet, a neural model which predicts clothing deformation in 3D as a function of three factors: pose, shape and style (garment geometry), while retaining wrinkle detail. This goes beyond prior models, which are either specific to one style and shape, or generalize to different shapes producing smooth results, despite being style specific. Our hypothesis is that (even non-linear) combinations of examples smooth out high frequency components such as fine-wrinkles, which makes learning the three factors jointly hard. At the heart of our technique is a decomposition of deformation into a high frequency and a low frequency component. While the low-frequency component is predicted from pose, shape and style parameters with an MLP, the high-frequency component is predicted with a mixture of shape-style specific pose models. The weights of the mixture are computed with a narrow bandwidth kernel to guarantee that only predictions with similar high-frequency patterns are combined. The style variation is obtained by computing, in a canonical pose, a subspace of deformation, which satisfies physical constraints such as inter-penetration, and draping on the body. TailorNet delivers 3D garments which retain the wrinkles from the physics based simulations (PBS) it is learned from, while running more than 1000 times faster. In contrast to PBS, TailorNet is easy to use and fully differentiable, which is crucial for computer vision algorithms. Several experiments demonstrate TailorNet produces more realistic results than prior work, and even generates temporally coherent deformations on sequences of the AMASS dataset, despite being trained on static poses from a different dataset. To stimulate further research in this direction, we will make a dataset consisting of 55800 frames, as well as our model publicly available at https://virtualhumans.mpi-inf.mpg.de/tailornet.

Citations (236)

Summary

  • The paper introduces a neural network that decomposes clothing deformations into low- and high-frequency components for detailed 3D predictions.
  • It achieves over 1000x speed improvements in generating realistic clothing animations while maintaining fine wrinkle details compared to traditional physics-based simulations.
  • TailorNet's differentiable design supports integration with machine learning pipelines, enhancing scalable virtual try-on systems and 3D animation workflows.

An Expert Analysis of "TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style"

The paper "TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape, and Garment Style" introduces a novel neural network model designed to predict three-dimensional clothing deformations influenced by human body pose, shape, and garment style. This work addresses the constraints of existing models, which are typically limited to specific styles or shapes, to offer a more comprehensive solution that retains the intricate details of clothing wrinkles.

Methodology and Model Highlights

TailorNet employs a sophisticated decomposition strategy, dividing clothing deformation into high-frequency and low-frequency components. The low-frequency deformations are captured using a simple multi-layer perceptron (MLP) model, while the high-frequency details are predicted through a mixture of specialized models. These high-frequency pose models are calibrated for specific combinations of body shapes and garment styles. A narrow bandwidth kernel determines the mixture weights to ensure that predictions retain the fine details characteristic of individual garment styles.

Strong Numerical Results

The authors demonstrate that TailorNet significantly accelerates the process of generating detailed clothing animations—reported to be over 1000 times faster than conventional physics-based simulations (PBS), without compromising the quality. This neural model presents detailed clothing dynamics that are essential for realistic 3D animations and can operate efficiently even on minimal computational resources.

Comparison with Existing Methods

The paper provides an extensive comparison of TailorNet with pre-existing approaches, underscoring its ability to model intertwined deformation effects from pose, shape, and style. Whereas prior methods often produce overly smoothed results, TailorNet is shown to effectively maintain high-frequency wrinkle details. This advancement speaks to the model’s robust performance across varying body shapes and styles, achieved through strategic decomposition into distinct mesh deformation components.

Implications and Future Developments

From a practical application perspective, TailorNet reduces the complexity and expert input generally required by PBS systems, facilitating seamless integration with both computer vision and machine learning pipelines. Its fully differentiable nature is foremost in addressing tasks that require backward gradient propagation within neural networks. The potential for scalable learning from real-world clothing datasets suggests avenues for continual enhancement and adaptation.

Looking to the future, prospective developments in AI can leverage TailorNet to automate the interactive virtual try-on systems and 3D content creation for media and entertainment industries. Further expansions could include accommodating variable fabric properties or integrating learning feedback loops that refine its predictive capabilities through real-world interaction data.

Conclusion

TailorNet stands as an innovative leap forward, incorporating pose, shape, and style variation into a single, accessible solution without sacrificing the intricacy of clothing details. Its high quantitative accuracy, ease of use, and processing efficiency mark it as a particularly relevant tool for applications necessitating detailed 3D clothing simulations. By making the model and dataset publicly available, ongoing research can benefit substantially from TailorNet’s methodological and computational advancements.