We present BackTrack, a trackpad placed on the back of a smartphone to track fine-grained finger motions. Our system has a small form factor, with all the circuits encapsulated in a thin layer attached to a phone case. It can be used with any off-the-shelf smartphone, requiring no power supply or modification of the operating systems. BackTrack simply extends the finger tracking area of the front screen, without interrupting the use of the front screen. It also provides a switch to prevent unintentional touch on the trackpad. All these features are enabled by a battery-free capacitive circuit, part of which is a transparent, thin-film conductor coated on a thin glass and attached to the front screen. To ensure accurate and robust tracking, the capacitive circuits are carefully designed. Our design is based on a circuit model of capacitive touchscreens, justified through both physics-based finite-element simulation and controlled laboratory experiments. We conduct user studies to evaluate the performance of using BackTrack. We also demonstrate its use in a number of smartphone applications.

Deep generative models of 3D shapes have received a great deal of research interest. Yet, almost all of them generate discrete shape representations, such as voxels, point clouds, and polygon meshes. We present the first 3D generative model for a drastically different shape representation— describing a shape as a sequence of computer-aided design (CAD) operations. Unlike meshes and point clouds, CAD models encode the user creation process of 3D shapes, widely used in numerous industrial and engineering design tasks. However, the sequential and irregular structure of CAD operations poses significant challenges for existing 3D generative models. Drawing an analogy between CAD operations and natural language, we propose a CAD generative network based on the Transformer. We demonstrate the performance of our model for both shape autoencoding and random shape generation. To train our network, we create a new CAD dataset consisting of 178,238 models and their CAD construction sequences. We have made this dataset publicly available to promote future research on this topic.

Camera tracking is an essential building block in a myriad of HCI applications. For example, commercial VR devices are equipped with dedicated hardware, such as laser-emitting beacon stations, to enable accurate tracking of VR headsets. However, this hardware remains costly. On the other hand, low-cost solutions such as IMU sensors and visual markers exist, but they suffer from large tracking errors. In this work, we bring high accuracy and low cost together to present MoiréBoard, a new 3-DOF camera position tracking method that leverages a seemingly irrelevant visual phenomenon, the moiré effect. Based on a systematic analysis of the moiré effect under camera projection, MoiréBoard requires no power nor camera calibration. It can be easily made at a low cost (e.g., through 3D printing), ready to use with any stock mobile devices with a camera. Its tracking algorithm is computationally efficient, able to run at a high frame rate. Although it is simple to implement, it tracks devices at high accuracy, comparable to the state-of-the-art commercial VR tracking systems.

Micro-appearance models have brought unprecedented fidelity and details to cloth rendering. Yet, these models neglect fabric mechanics: when a piece of cloth interacts with the environment, its yarn and fiber arrangement usually changes in response to external contact and tension forces. Since subtle changes of a fabric's microstructures can greatly affect its macroscopic appearance, mechanics-driven appearance variation of fabrics has been a phenomenon that remains to be captured. We introduce a mechanics-aware model that adapts the microstructures of cloth yarns in a physics-based manner. Our technique works on two distinct physical scales: using physics-based simulations of individual yarns, we capture the rearrangement of yarn-level structures in response to external forces. These yarn structures are further enriched to obtain appearance-driving fiber-level details. The cross-scale enrichment is made practical through a new parameter fitting algorithm for simulation, an augmented procedural yarn model coupled with a custom-design regression neural network. We train the network using a dataset generated by joint simulations at both the yarn and the fiber levels. Through several examples, we demonstrate that our model is capable of synthesizing photorealistic cloth appearance in a mechanically plausible way.

Modern image classification systems are often built on deep neural networks, which suffer from adversarial examples--images with deliberately crafted, imperceptible noise to mislead the network's classification. To defend against adversarial examples, a plausible idea is to obfuscate the network's gradient with respect to the input image. This general idea has inspired a long line of defense methods. Yet, almost all of them have proven vulnerable. We revisit this seemingly flawed idea from a radically different perspective. We embrace the omnipresence of adversarial examples and the numerical procedure of crafting them, and turn this harmful attacking process into a useful defense mechanism. Our defense method is conceptually simple: before feeding an input image for classification, transform it by finding an adversarial example on a pre-trained external model. We evaluate our method against a wide range of possible attacks. On both CIFAR-10 and Tiny ImageNet datasets, our method is significantly more robust than state-of-the-art methods. Particularly, in comparison to adversarial training, our method offers lower training cost as well as stronger robustness.

Many generative models have to combat missing modes. The conventional wisdom to this end is by reducing through training a statistical distance (such as f -divergence) between the generated distribution and provided data distribution. But this is more of a heuristic than a guarantee. The statistical distance measures a global, but not local, similarity between two distributions. Even if it is small, it does not imply a plausible mode coverage. Rethinking this problem from a game-theoretic perspective, we show that a complete mode coverage is firmly attainable. If a generative model can approximate a data distribution moderately well under a global statistical distance measure, then we will be able to find a mixture of generators that collectively covers every data point and thus every mode, with a lower-bounded generation probability. Constructing the generator mixture has a connection to the multiplicative weights update rule, upon which we propose our algorithm. We prove that our algorithm guarantees complete mode coverage. And our experiments on real and synthetic datasets confirm better mode coverage over recent approaches, ones that also use generator mixtures but rely on global statistical distances.

We present Vidgets , a family of mechanical widgets, specifically push buttons and rotary knobs that augment mobile devices with tangible user interfaces. When these widgets are attached to a mobile device and a user interacts with them, the widgets' nonlinear mechanical response shifts the device slightly and quickly, and this subtle motion can be detected by the accelerometer commonly equipped on mobile devices. We propose a physics-based model to understand the nonlinear mechanical response of widgets. This understanding enables us to design tactile force profiles of these widgets so that the resulting accelerometer signals become easy to recognize. We then develop a lightweight signal processing algorithm that analyzes the accelerometer signals and recognizes how the user interacts with the widgets in real time. Vidgets widgets are low-cost, compact, reconfigurable, and power efficient. They can form a diverse set of physical interfaces that enrich users' interactions with mobile devices in various practical scenarios. We demonstrate their use in three applications: photo capture with single-handed zoom, control of mobile games, and making a playable mobile music instrument.

This paper addresses the mode collapse for generative adversarial networks (GANs). We view modes as a geometric structure of data distribution in a metric space. Under this geometric lens, we embed subsamples of the dataset from an arbitrary metric space into the L2 space, while preserving their pairwise distance distribution. Not only does this metric embedding determine the dimensionality of the latent space automatically, it also enables us to construct a mixture of Gaussians to draw latent space random vectors. We use the Gaussian mixture model in tandem with a simple augmentation of the objective function to train GANs. Every major step of our method is supported by theoretical analysis, and our experiments on real and synthetic data confirm that the generator is able to produce samples spreading over most of the modes while avoiding unwanted samples, outperforming several recent GAN variants on a number of metrics and offering new features.

We introduceFontCode, an information embedding technique for text documents. Provided a text document with specific fonts, our method embeds user-specified information in the text by perturbing the glyphs of text characters while preserving the text content. We devise an algorithm to choose unobtrusive yet machine-recognizable glyph perturbations, leveraging a recently developed generative model that alters the glyphs of each character continuously on a font manifold. We then introduce an algorithm that embeds a user-provided message in the text document and produces an encoded document whose appearance is minimally perturbed from the original document. We also present a glyph recognition method that recovers the embedded information from an encoded document stored as a vector graphic or pixel image, or even on a printed paper. In addition, we introduce a new error-correction coding scheme that rectifies a certain number of recognition errors. Lastly, we demonstrate that our technique enables a wide array of applications, using it as a text document metadata holder, an unobtrusive optical barcode, a cryptographic message embedding scheme, and a text document signature.