Learning 3D Garment Animation from Trajectories of A Piece of Cloth

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TL;DR

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Key Takeaways

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Why does it matter?

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This paper is important because it presents a novel disentangled learning framework for 3D garment animation, addressing the limitations of data-hungry methods. It introduces EUNet, a network that learns constitutive behaviors from a single piece of cloth, enabling generalization to various garments. This work opens avenues for more efficient and realistic garment animation in various applications, including virtual reality, gaming, and fashion design. The energy-based optimization method improves the physical plausibility of animations. The method is highly significant due to its data efficiency and potential to improve the quality of virtual garments.

Visual Insights

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This figure demonstrates the core idea of the paper. The left shows a single piece of cloth whose movement is observed and used to learn the physical properties of the material. This learned information is then used to simulate the realistic movement of different garments (middle and right) made from that same material, showcasing the ability to animate various clothing items using only data from a simple cloth.

This table presents the quantitative results of the EUNet model’s performance. It shows the mean and standard deviation of square errors calculated using Equation 8, comparing the full EUNet model with versions that exclude the dissipation unit (Φd) and the contrastive loss (Lcon). The results are broken down by material type (Silk, Leather, Denim, Cotton) and overall performance. Lower values indicate better performance.

In-depth insights

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Disentangled Learning

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Disentangled learning tackles the challenge of separating intertwined factors within complex data, a crucial aspect in various machine learning applications. The core idea revolves around learning representations where individual factors are independent of one another, improving model interpretability and generalization. In the context of garment animation, disentangling the constitutive laws (material properties) from the dynamic motions (external forces) is particularly insightful. This approach allows for learning material behavior from a simpler source, like a single piece of cloth, and then applying those learned properties to animate diverse garments. This strategy offers significant advantages, as it reduces the need for large, garment-specific datasets typically required for traditional supervised learning methods. Furthermore, it enables generalization to unseen garment types and conditions, significantly enhancing the efficiency and scalability of garment animation models. The effectiveness of this method depends on the success of disentanglement and the capacity of the learned representations to accurately capture the essential physics. Successfully disentangling these factors promises to yield more robust, efficient, and physically plausible results compared to traditional, monolithic approaches.

EUNet: Energy Unit Net

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The proposed EUNet (Energy Unit Network) offers a novel approach to learning constitutive laws for garment animation. Instead of relying on traditional physics models or large-scale garment datasets, EUNet directly learns constitutive behavior from the observed trajectories of a single piece of cloth. This disentangled approach significantly reduces data requirements and enhances generalization. The network models the constitutive relations in the form of energy, effectively capturing the energy changes due to deformations like stretching and bending. EUNet’s edge-wise energy unit design is topology-agnostic, making it applicable to diverse garment types. Furthermore, the incorporation of vertex-wise contrastive loss enhances the accuracy of energy gradient predictions. By constraining energy optimization with the learned energy, the model achieves stable and physically plausible garment animations, even with long-term predictions. This innovative approach makes garment animation more efficient, robust, and generalizable by decoupling material properties from garment topology.

Constitutive Behavior

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The concept of ‘Constitutive Behavior’ in a material science context refers to the relationship between stress and strain within a material. This relationship dictates how a material responds to external forces, defining its mechanical properties. In the context of garment animation, accurately modeling constitutive behavior is crucial for realistic simulations. Traditional approaches often rely on analytical physics models, requiring the estimation of material parameters that are often difficult to measure or obtain. Data-driven methods offer an alternative, learning constitutive behavior directly from observed garment movements. However, these methods can be data-hungry, and overfitting to specific garments might limit generalization. A promising direction is to disentangle the learning process, separating the learning of constitutive laws from the task of garment animation. By learning constitutive laws from simple cloth dynamics, models can achieve greater generalization and reduce data requirements. This disentanglement enables the use of simpler training data (e.g., a single piece of cloth) and allows for the animation of diverse garments sharing the same material properties. Energy Unit Networks, for example, are capable of learning the energy associated with deformations and thus provide gradients necessary for physics-based animation, achieving physically plausible results.

Garment Animation

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Garment animation, a crucial aspect of virtual reality, gaming, and film, has traditionally relied on physics-based simulation methods that are computationally expensive and struggle to generalize across different garments and materials. Deep learning approaches offer a promising alternative, capable of learning complex garment dynamics from data and generating realistic animations with greater efficiency. However, existing data-driven methods often require massive datasets, which are costly to obtain. This paper tackles this limitation by proposing a disentangled approach that separates the learning of material properties (constitutive relations) from the animation process itself. By learning constitutive behaviors from observations of a simple piece of cloth, the method generalizes effectively to unseen garment types, thereby reducing the need for large-scale, garment-specific datasets. This approach leverages energy optimization constrained by a novel Energy Unit Network (EUNet) to effectively animate various garments. EUNet directly captures constitutive behaviors in the form of energy, avoiding reliance on pre-defined physics models, thereby showcasing a more robust and flexible technique. The resulting disentangled framework demonstrates superior performance compared to existing garment-wise supervised learning methods, delivering stable and physically plausible animations, particularly in long-term predictions.

Data Efficiency

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The core concept of data efficiency in this research centers on reducing the reliance on extensive, garment-specific datasets for training garment animation models. Traditional methods necessitate vast amounts of data, representing diverse garments, poses, and materials, significantly increasing time and cost. This paper introduces a disentangled learning approach, separating the learning process into two stages. First, a constitutive model is learned using data from a single piece of cloth, capturing inherent material properties. Second, this learned model is applied to various garment animations, significantly reducing the need for garment-specific training data. This strategy showcases high efficiency by leveraging the shared constitutive properties across different garment types. The benefit lies in generalizability to unseen garment topologies and materials, allowing for more flexible and cost-effective training of accurate garment animation models. The effectiveness is demonstrated through experimental comparisons, highlighting the superior performance of this data-efficient approach compared to traditional garment-wise supervised learning.

More visual insights

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More on figures

This figure illustrates the two-step disentangled learning approach for garment animation. First, the EUNet learns constitutive relations from a single piece of cloth’s trajectory, capturing energy changes from various deformations without using analytical physics models. Then, this learned information is used to constrain energy optimization for animating various garments, resulting in realistic movements.

This figure illustrates the two-step disentangled learning approach used in the paper. First, a constitutive model (EUNet) is trained on a single piece of cloth to learn the relationship between deformation and energy. Then, this model is used to animate various garments by constraining energy optimization, allowing the animation to inherit material properties from the learned model.

This figure shows a qualitative comparison of garment animation results. The top row displays ground truth garment movements. The middle and bottom rows show results from models trained using the proposed disentangled learning scheme, demonstrating their ability to generate realistic garment animations that closely match the ground truth, even for complex movements and over longer time periods.

This figure shows qualitative results comparing the garment animation generated by models trained using the proposed disentangled learning scheme (MGN-S and MGN-H with EUNet) against baselines. The results demonstrate that the proposed method achieves more realistic and robust garment animation, even for long-term predictions, by leveraging the learned constitutive relations from a single piece of cloth.

This figure shows the training data used for the EUNet model in the paper. It showcases five different time steps (T=1, T=7, T=13, T=19, T=25) in the simulated movement of two pieces of cloth, one made of leather and the other of silk. Both cloths are pinned at two corners, and their dynamic behaviors under gravity are captured. The purpose of this data is to train the EUNet model to learn the constitutive relationships between deformation and energy, which is independent of garment topology.

This figure shows how the angles between vertex normals are represented. The angles αeij and βeij are defined to describe the change in orientation of the vertex normals (ni and nt) relative to the edge eij. αeij represents a rotation around the edge, and βeij represents the angle between the rotated normal and the original normal within the plane formed by the edge and the original normal. This method helps in capturing both bending and stretching deformations.

This figure demonstrates how a small change (noise) added to one vertex affects the energy of its neighboring vertices and their connecting edges. The left panel (a) shows the undisturbed mesh. The right panel (b) shows that when a noise is applied to vertex i (red), it changes the normal vectors of the nearby orange vertices (j ∈ Ni). Consequently, the edge energy units between vertex i and j ∈ Ni (orange edges) also change.

This figure displays qualitative results of garment animation using the proposed disentangled training scheme. Models (MGN-S and MGN-H) constrained by the Energy Unit Network (EUNet) show similar deformation patterns to the ground truth garments, demonstrating the effectiveness of learning constitutive relations from a single piece of cloth. The results highlight the model’s ability to generate realistic wrinkles and interactions with a human body, even in long-term predictions, unlike garment-wise trained models.

More on tables

This table compares the performance of different garment animation methods on the Cloth3D dataset. The methods include those using garment-wise supervised learning (MGN and LayersNet), a physics-based model (MGN-S+PHYS), and the proposed disentangled approach using EUNet (MGN-S+EUNet and MGN-H+EUNet). The metrics reported are Euclidean error (in mm) and collision rate (%). The results show that the methods using EUNet achieve better performance, especially in terms of collision rate, suggesting that the disentangled approach leads to more physically plausible garment animations.

This table compares the performance of different garment animation methods on the Cloth3D dataset. It shows Euclidean error and collision rates for various methods, including those using garment-wise learning and the proposed disentangled approach with EUNet. The results demonstrate that models using the EUNet-based disentangled scheme achieve superior performance compared to other methods, especially when considering long-term predictions and physical plausibility.

This table presents a comparison of Euclidean error and collision rates for different garment animation methods on the Cloth3D dataset. The methods include those using garment-wise supervised learning (MGN and LayersNet), physics-based simulations (MGN-S+PHYS), and the proposed disentangled approach combining EUNet with energy optimization (MGN-S+EUNet and MGN-H+EUNet). The results show the superior performance of the proposed method in terms of lower errors and collision rates, even without access to ground truth garment data.

This table presents the quantitative results of the EUNet model’s performance. It compares the mean and standard deviation of square errors obtained under different configurations of the model: with and without the dissipation unit and contrastive loss. The results demonstrate the impact of these components on the model’s accuracy and generalization ability.

Full paper

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