Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed humans and thus do not capture the complexity of dressed humans in common images and videos. To address this, we learn a generative 3D mesh model of clothing from 3D scans of people with varying pose. Going beyond previous work, our generative model is conditioned on different clothing types, giving the ability to dress different body shapes in a variety of clothing. To do so, we train a conditional Mesh-VAE-GAN on clothing displacements from a 3D SMPL body model. This generative clothing model enables us to sample various types of clothing, in novel poses, on top of SMPL. With a focus on clothing geometry, the model captures both global shape and local structure, effectively extending the SMPL model to add clothing. To our knowledge, this is the first conditional VAE-GAN that works on 3D meshes. For clothing specifically, it is the first such model that directly dresses 3D human body meshes and generalizes to different poses.
We present a perceptual control space for simulation of cloth that works with any physical simulator, treating it as a black box. The perceptual control space provides intuitive, art-directable control over the simulation behavior based on a learned mapping from common descriptors for cloth (e.g., flowiness, softness) to the parameters of the simulation. To learn the mapping, we perform a series of perceptual experiments in which the simulation parameters are varied and participants assess the values of the common terms of the cloth on a scale. A multi-dimensional sub-space regression is performed on the results to build a perceptual generative model over the simulator parameters. We evaluate the perceptual control space by demonstrating that the generative model does in fact create simulated clothing that is rated by participants as having the expected properties. We also show that this perceptual control space generalizes to garments and motions not in the original experiments. This paper presents a learning-based clothing animation method for highly efficient virtual try-on simulation. Given a garment, we preprocess a rich database of physically-based dressed character simulations, for multiple body shapes and animations. Then, using this database, we train a learning-based model of cloth drape and wrinkles, as a function of body shape and dynamics. We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape. We use a recurrent neural network to regress garment wrinkles, and we achieve highly plausible nonlinear effects, in contrast to the blending artifacts suffered by previous methods. At runtime, dynamic virtual try-on animations are produced in just a few milliseconds for garments with thousands of triangles. We show qualitative and quantitative analysis of results We describe a complete system for animating realistic clothing on synthetic bodies of any shape and pose without manual intervention. The key component of the method is a model of clothing called DRAPE (DRessing Any PErson) that is learned from a physics-based simulation of clothing on bodies of different shapes and poses. The DRAPE model has the desirable property of "factoring" clothing deformations due to body shape from those due to pose variation. This factorization provides an approximation to the physical clothing deformation and greatly simplifies clothing synthesis. Given a parameterized model of the human body with known shape and pose parameters, we describe an algorithm that dresses the body with a garment that is customized to fit and possesses realistic wrinkles. DRAPE can be used to dress static bodies or animated sequences with a learned model of the cloth dynamics. Since the method is fully automated, it is appropriate for dressing large numbers of virtual characters of varying shape. The method is significantly more efficient than physical simulation. Designing real and virtual garments is becoming extremely demanding with rapidly changing fashion trends and increasing need for synthesizing realisticly dressed digital humans for various applications. This necessitates creating simple and effective workflows to facilitate authoring sewing patterns customized to garment and target body shapes to achieve desired looks. Traditional workflow involves a trial-and-error procedure wherein a mannequin is draped to judge the resultant folds and the sewing pattern iteratively adjusted until the desired look is achieved. This requires time and experience. Instead, we present a data-driven approach wherein the user directly indicates desired fold patterns simply by sketching while our system estimates corresponding garment and body shape parameters at interactive rates. The recovered parameters can then be further edited and the updated draped garment previewed. Technically, we achieve this via a novel shared shape space that allows the user to seamlessly specify desired characteristics across multimodal input without requiring to run garment simulation at design time. We evaluate our approach qualitatively via a user study and quantitatively against test datasets, and demonstrate how our system can generate a rich quality of on-body garments targeted for a range of body shapes while achieving desired fold characteristics. Code and data are available at our project webpage. The central argument against data-driven methods in computer graphics rests on the curse of dimensionality: it is intractable to precompute "everything" about a complex space. In this paper, we challenge that assumption by using several thousand CPU-hours to perform a massive exploration of the space of secondary clothing effects on a character animated through a large motion graph. Our system continually explores the phase space of cloth dynamics, incrementally constructing a secondary cloth motion graph that captures the dynamics of the system. We find that it is possible to sample the dynamical space to a low visual error tolerance and that secondary motion graphs containing tens of gigabytes of raw mesh data can be compressed down to only tens of megabytes. These results allow us to capture the effect of high-resolution, off-line cloth simulation for a rich space of character motion and deliver it efficiently as part of an interactive application. We present a technique for learning clothing models that enables the simultaneous animation of thousands of detailed garments in real-time. This surprisingly simple conditional model learns and preserves the key dynamic properties of a cloth motion along with folding details. Our approach requires no a priori physical model, but rather treats training data as a "black box." We show that the models learned with our method are stable over large time-steps and can approximately resolve cloth-body collisions. We also show that within a class of methods, no simpler model covers the full range of cloth dynamics captured by ours. Our method bridges the current gap between skinning and physical simulation, combining benefits of speed from the former with dynamic effects from the latter. We demonstrate our approach on a variety of apparel worn by male and female human characters performing a varied set of motions typically used in video games (e.g., walking, running, jumping, etc.). Highly realistic virtual human models are rapidly becoming commonplace in computer graphics. These models, often represented by complex shape and requiring labor-intensive process, challenge the problem of automatic modeling. The problem and solutions to automatic modeling of animatable virtual humans are studied. Methods for capturing the shape of real people, parameterization techniques for modeling static shape (the variety of human body shapes) and dynamic shape (how the body shape changes as it moves) of virtual humans are classified, summarized and compared. Finally, methods for clothed virtual humans are reviewed.