Dynamic Garments Research Articles

High-Quality Animatable Dynamic Garment Reconstruction From Monocular Videos

High-Quality Animatable Dynamic Garment Reconstruction From Monocular Videos

Neural Garment Dynamics via Manifold‐Aware Transformers

AbstractData driven and learning based solutions for modeling dynamic garments have significantly advanced, especially in the context of digital humans. However, existing approaches often focus on modeling garments with respect to a fixed parametric human body model and are limited to garment geometries that were seen during training. In this work, we take a different approach and model the dynamics of a garment by exploiting its local interactions with the underlying human body. Specifically, as the body moves, we detect local garment‐body collisions, which drive the deformation of the garment. At the core of our approach is a mesh‐agnostic garment representation and a manifold‐aware transformer network design, which together enable our method to generalize to unseen garment and body geometries. We evaluate our approach on a wide variety of garment types and motion sequences and provide competitive qualitative and quantitative results with respect to the state of the art.

Generating diverse clothed 3D human animations via a generative model

Data-driven garment animation is a current topic of interest in the computer graphics industry. Existing approaches generally establish the mapping between a single human pose or a temporal pose sequence, and garment deformation, but it is difficult to quickly generate diverse clothed human animations. We address this problem with a method to automatically synthesize dressed human animations with temporal consistency from a specified human motion label. At the heart of our method is a two-stage strategy. Specifically, we first learn a latent space encoding the sequence-level distribution of human motions utilizing a transformer-based conditional variational autoencoder (Transformer-CVAE). Then a garment simulator synthesizes dynamic garment shapes using a transformer encoder–decoder architecture. Since the learned latent space comes from varied human motions, our method can generate a variety of styles of motions given a specific motion label. By means of a novel beginning of sequence (BOS) learning strategy and a self-supervised refinement procedure, our garment simulator is capable of efficiently synthesizing garment deformation sequences corresponding to the generated human motions while maintaining temporal and spatial consistency. We verify our ideas experimentally. This is the first generative model that directly dresses human animation.

Motion Guided Deep Dynamic 3D Garments

Realistic dynamic garments on animated characters have many AR/VR applications. While authoring such dynamic garment geometry is still a challenging task, data-driven simulation provides an attractive alternative, especially if it can be controlled simply using the motion of the underlying character. In this work, we focus on motion guided dynamic 3D garments, especially for loose garments. In a data-driven setup, we first learn a generative space of plausible garment geometries. Then, we learn a mapping to this space to capture the motion dependent dynamic deformations, conditioned on the previous state of the garment as well as its relative position with respect to the underlying body. Technically, we model garment dynamics, driven using the input character motion, by predicting per-frame local displacements in a canonical state of the garment that is enriched with frame-dependent skinning weights to bring the garment to the global space. We resolve any remaining per-frame collisions by predicting residual local displacements. The resultant garment geometry is used as history to enable iterative roll-out prediction. We demonstrate plausible generalization to unseen body shapes and motion inputs, and show improvements over multiple state-of-the-art alternatives. Code and data is released in https://geometry.cs.ucl.ac.uk/projects/2022/MotionDeepGarment/

Dynamic neural garments

A vital task of the wider digital human effort is the creation of realistic garments on digital avatars, both in the form of characteristic fold patterns and wrinkles in static frames as well as richness of garment dynamics under avatars' motion. Existing workflow of modeling, simulation, and rendering closely replicates the physics behind real garments, but is tedious and requires repeating most of the workflow under changes to characters' motion, camera angle, or garment resizing. Although data-driven solutions exist, they either focus on static scenarios or only handle dynamics of tight garments. We present a solution that, at test time, takes in body joint motion to directly produce realistic dynamic garment image sequences. Specifically, given the target joint motion sequence of an avatar, we propose dynamic neural garments to synthesize plausible dynamic garment appearance from a desired viewpoint. Technically, our solution generates a coarse garment proxy sequence, learns deep dynamic features attached to this template, and neurally renders the features to produce appearance changes such as folds, wrinkles, and silhouettes. We demonstrate generalization behavior to both unseen motion and unseen camera views. Further, our network can be fine-tuned to adopt to new body shape and/or background images. We demonstrate our method on a wide range of real and synthetic garments. We also provide comparisons against existing neural rendering and image sequence translation approaches, and report clear quantitative and qualitative improvements. Project page: http://geometry.cs.ucl.ac.uk/projects/2021/DynamicNeuralGarments/

An Overview Of The Research Status And Development Trend Of Dynamic Pressure Distribution Testing For Clothing

Clothing pressure can be divided into dynamic pressure and static pressure. When measuring dynamic garment pressure, it is difficult to accurately measure dynamic garment pressure due to the complexity of measuring device and the influence factors such as sliding between skin and fabric during movement. To understand the influencing factors of clothing pressure, clothing pressure measurement technology and the existing dynamic pressure distribution prediction model, so as to achieve accurate prediction of dynamic pressure distribution of clothing, can provide ideas for future research on dynamic pressure comfort of clothing and provide scientific basis for clothing design.

Learning an intrinsic garment space for interactive authoring of garment animation

Authoring dynamic garment shapes for character animation on body motion is one of the fundamental steps in the CG industry. Established workflows are either time and labor consuming (i.e., manual editing on dense frames with controllers), or lack keyframe-level control (i.e., physically-based simulation). Not surprisingly, garment authoring remains a bottleneck in many production pipelines. Instead, we present a deep-learning-based approach for semi-automatic authoring of garment animation, wherein the user provides the desired garment shape in a selection of keyframes, while our system infers a latent representation for its motion-independent intrinsic parameters (e.g., gravity, cloth materials, etc.). Given new character motions, the latent representation allows to automatically generate a plausible garment animation at interactive rates. Having factored out character motion, the learned intrinsic garment space enables smooth transition between keyframes on a new motion sequence. Technically, we learn an intrinsic garment space with an motion-driven autoencoder network, where the encoder maps the garment shapes to the intrinsic space under the condition of body motions, while the decoder acts as a differentiable simulator to generate garment shapes according to changes in character body motion and intrinsic parameters. We evaluate our approach qualitatively and quantitatively on common garment types. Experiments demonstrate our system can significantly improve current garment authoring workflows via an interactive user interface. Compared with the standard CG pipeline, our system significantly reduces the ratio of required keyframes from 20% to 1 -- 2%.

DeepGarment : 3D Garment Shape Estimation from a Single Image

3D garment capture is an important component for various applications such as free-view point video, virtual avatars, online shopping, and virtual cloth fitting. Due to the complexity of the deformations, capturing 3D garment shapes requires controlled and specialized setups. A viable alternative is image-based garment capture. Capturing 3D garment shapes from a single image, however, is a challenging problem and the current solutions come with assumptions on the lighting, camera calibration, complexity of human or mannequin poses considered, and more importantly a stable physical state for the garment and the underlying human body. In addition, most of the works require manual interaction and exhibit high run-times. We propose a new technique that overcomes these limitations, making garment shape estimation from an image a practical approach for dynamic garment capture. Starting from synthetic garment shape data generated through physically based simulations from various human bodies in complex poses obtained through Mocap sequences, and rendered under varying camera positions and lighting conditions, our novel method learns a mapping from rendered garment images to the underlying 3D garment model. This is achieved by training Convolutional Neural Networks CNN-s to estimate 3D vertex displacements from a template mesh with a specialized loss function. We illustrate that this technique is able to recover the global shape of dynamic 3D garments from a single image under varying factors such as challenging human poses, self occlusions, various camera poses and lighting conditions, at interactive rates. Improvement is shown if more than one view is integrated. Additionally, we show applications of our method to videos.

Dynamic Garment Simulation based on Hybrid Bounding Volume Hierarchy

Abstract In order to solve the computing speed and efficiency problem of existing dynamic clothing simulation, this paper presents a dynamic garment simulation based on a hybrid bounding volume hierarchy. It firstly uses MCASG graph theory to do the primary segmentation for a given three-dimensional human body model. And then it applies K-means cluster to do the secondary segmentation to collect the human body’s upper arms, lower arms, upper legs, lower legs, trunk, hip and woman’s chest as the elementary units of dynamic clothing simulation. According to different shapes of these elementary units, it chooses the closest and most efficient hybrid bounding box to specify these units, such as cylinder bounding box and elliptic cylinder bounding box. During the process of constructing these bounding boxes, it uses the least squares method and slices of the human body to get the related parameters. This approach makes it possible to use the least amount of bounding boxes to create close collision detection regions for the appearance of the human body. A spring-mass model based on a triangular mesh of the clothing model is finally constructed for dynamic simulation. The simulation result shows the feasibility and superiority of the method described.

Low Spring Index NiTi Coil Actuators for Use in Active Compression Garments

This paper describes the modeling, development, and testing of low spring index nickel titanium (NiTi) coil actuators designed for use in wearable compression garments, and presents a prototype tourniquet system using these actuators. NiTi coil actuators produce both large forces (>1 N) and large recoverable displacements (>100% length) that are well suited for compression garment design. Thermomechanical coil models are presented that describe temperature and force as a function of nondimensionalized coil geometry, extensional strain, and applied voltage. These models suggest that low spring index coils maximize activation force, and an analytical model is presented to predict garment counter-pressure based on actuator architecture. Several low spring index ( C $=$ 3.08) coils were manufactured, annealed, and tested to assess their detwinning and activation characteristics. Results suggest both annealing and applied stress affect activation thresholds. Actuator force increases both with extensional strain and applied voltage up to 7.24 N. A first-generation compression tourniquet system using integrated actuators with direct voltage control of applied pressure is presented, demonstrating >70% increase in applied pressure during activation. This approach enables new, dynamic garments with controllable activation and low effort donning and doffing, with applications ranging from healthcare solutions to advanced space suit design.

Numerical Simulation of 3D Dynamic Garment Pressure

This paper presents a mechanical model for numerical simulations of 3D dynamic garment pressure during wear using a finite element method. Based on analyzing the contact characteristics between the human body and the garment, a mechanical model is developed based on the theory of dynamic contact mechanics. The garment is regarded as an elastic shell of geometric nonlinearity and the human body is assumed to be rigid. The contact between body and garment is modeled as a dynamic sliding interface. A series of examples is presented to illustrate the simulation results of the computational model using commercial finite element software: a female human model wearing a set of perfectly fitting sportswear. The garments have the same style but are made from different materials: a cotton denim fabric and a knitted nylon fabric. Compared with values of measured garment pressure reported in the literature, the predicted pressure is close to the magnitude of experimental measurements, indicating that the model is able to simulate garment pressure during wear with reasonable accuracy. The computational model can simulate and predict the dynamic mechanical behavior of garments during wear, such as garment deformation, pressure, and internal stresses, without actually producing the garment.