For machines to interact with the physical world, they must understand the physical properties of objects and materials they encounter. We use fabrics as an example of a deformable material with a rich set of mechanical properties. A thin flexible fabric, when draped, tends to look different from a heavy stiff fabric. It also feels different when touched. Using a collection of 118 fabric sample, we captured color and depth images of draped fabrics along with tactile data from a high resolution touch sensor. We then sought to associate the information from vision and touch by jointly training CNNs across the three modalities. Through the CNN, each input, regardless of the modality, generates an embedding vector that records the fabric's physical property. By comparing the embeddings, our system is able to look at a fabric image and predict how it will feel, and vice versa. We also show that a system jointly trained on vision and touch data can outperform a similar system trained only on visual data when tested purely with visual inputs.
Researchers in computer vision and graphics have been trying to track or represent fabrics or clothes, but their visual representation is difficult to obtain compared with rigid objects', and the uncertainty and complexity of the shapes and motion make the fabrics or clothes more difficult to predict. To track the exact shape of the clothes, White al @cite_11 made dense patterns on fabrics or clothes, and used multiple cameras to track their motion thus to reconstruct the 3D shapes of the clothes. Han al @cite_15 represented the cloth shape with a 2-layer model: one represents the general shape, the other one represents fold shapes, which are measured by shape-from-shading methods. Some other researches tried to represent the fabrics by physical parameters, and estimated the parameters from the visual appearance. Baht al @cite_21 used a model made of physical properties, including density, bend stiffness, stretch stiffness, damping resistance and friction, to describe and simulate clothes. They estimate the properties by comparing the real clothes' motion video with the simulated videos. Bouman al @cite_17 measured fabric properties (stiffness and density) directly from the video of fabric motion using hand-crafted features, when the fabrics were hanged and exposed to different winds.
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In this paper, we present a two-level generative model for representing the images and surface depth maps of drapery and clothes. The upper level consists of a number of folds which will generate the high contrast (ridge) areas with a dictionary of shading primitives (for 2D images) and fold primitives (for 3D depth maps). These primitives are represented in parametric forms and are learned in a supervised learning phase using 3D surfaces of clothes acquired through photometric stereo. The lower level consists of the remaining flat areas which fill between the folds with a smoothness prior (Markov random field). We show that the classical ill-posed problem-shape from shading (SFS) can be much improved by this two-level model for its reduced dimensionality and incorporation of middle-level visual knowledge, i.e., the dictionary of primitives. Given an input image, we first infer the folds and compute a sketch graph using a sketch pursuit algorithm as in the primal sketch (, 2003). The 3D folds are estimated by parameter fitting using the fold dictionary and they form the "skeleton" of the drapery cloth surfaces. Then, the lower level is computed by conventional SFS method using the fold areas as boundary conditions. The two levels interact at the final stage by optimizing a joint Bayesian posterior probability on the depth map. We show a number of experiments which demonstrate more robust results in comparison with state-of-the-art work. In a broader scope, our representation can be viewed as a two-level inhomogeneous MRF model which is applicable to general shape-from-X problems. Our study is an attempt to revisit Marr's idea (Marr and Freeman, 1982) of computing the 2frac12D sketch from primal sketch. In a companion paper (Barbu and Zhu, 2005), we study shape from stereo based on a similar two-level generative sketch representation. Cloth simulations are notoriously difficult to tune due to the many parameters that must be adjusted to achieve the look of a particular fabric. In this paper, we present an algorithm for estimating the parameters of a cloth simulation from video data of real fabric. A perceptually motivated metric based on matching between folds is used to compare video of real cloth with simulation. This metric compares two video sequences of cloth and returns a number that measures the differences in their folds. Simulated annealing is used to minimize the frame by frame error between the metric for a given simulation and the real-world footage. To estimate all the cloth parameters, we identify simple static and dynamic calibration experiments that use small swatches of the fabric. To demonstrate the power of this approach, we use our algorithm to find the parameters for four different fabrics. We show the match between the video footage and simulated motion on the calibration experiments, on new video sequences for the swatches, and on a simulation of a full skirt. Passively estimating the intrinsic material properties of deformable objects moving in a natural environment is essential for scene understanding. We present a framework to automatically analyze videos of fabrics moving under various unknown wind forces, and recover two key material properties of the fabric: stiffness and area weight. We extend features previously developed to compactly represent static image textures to describe video textures, such as fabric motion. A discriminatively trained regression model is then used to predict the physical properties of fabric from these features. The success of our model is demonstrated on a new, publicly available database of fabric videos with corresponding measured ground truth material properties. We show that our predictions are well correlated with ground truth measurements of stiffness and density for the fabrics. Our contributions include: (a) a database that can be used for training and testing algorithms for passively predicting fabric properties from video, (b) an algorithm for predicting the material properties of fabric from a video, and (c) a perceptual study of humans' ability to estimate the material properties of fabric from videos and images. We capture the shape of moving cloth using a custom set of color markers printed on the surface of the cloth. The output is a sequence of triangle meshes with static connectivity and with detail at the scale of individual markers in both smooth and folded regions. We compute markers' coordinates in space using correspondence across multiple synchronized video cameras. Correspondence is determined from color information in small neighborhoods and refined using a novel strain pruning process. Final correspondence does not require neighborhood information. We use a novel data driven hole-filling technique to fill occluded regions. Our results include several challenging examples: a wrinkled shirt sleeve, a dancing pair of pants, and a rag tossed onto a cup. Finally, we demonstrate that cloth capture is reusable by animating a pair of pants using human motion capture data.