Disney Research has been actively pushing the state-of-the-art in digitizing humans over the past decade, impacting both academia and industry. In this talk I will give an overview of a selected few projects in this area, from research into production. I will be talking about photogrammetric shape acquisition and dense performance capture for faces, eye and teeth scanning and parameterization, as well as physically based capture and modelling for hair and volumetric tissues.
Organizers: Timo Bolkart
Abstract: Sequential Monte Carlo (SMC) methods (including the particle filters and smoothers) allows us to compute probabilistic representations of the unknown objects in models used to represent for example nonlinear dynamical systems. This talk has three connected parts: 1. A (hopefully pedagogical) introduction to probabilistic modelling of dynamical systems and an explanation of the SMC method. 2. In learning unknown parameters appearing in nonlinear state-space models using maximum likelihood it is natural to make use of SMC to compute unbiased estimates of the intractable likelihood. The challenge is that the resulting optimization problem is stochastic, which recently inspired us to construct a new solution to this problem. 3. A challenge with the above (and in fact with most use of SMC) is that it all quickly becomes very technical. This is indeed the key challenging in spreading the use of SMC methods to a wider group of users. At the same time there are many researchers who would benefit a lot from having access to these methods in their daily work and for those of us already working with them it is essential to reduce the amount of time spent on new problems. We believe that the solution to this can be provided by probabilistic programming. We are currently developing a new probabilistic programming language that we call Birch. A pre-release is available from birch-lang.org/ It allow users to use SMC methods without having to implement the algorithms on their own.
Organizers: Philipp Hennig
Today’s advances in tactile sensing and wearable, IOT and context-aware computing are spurring new ideas about how to configure touch-centered interactions in terms of roles and utility, which in turn expose new technical and social design questions. But while haptic actuation, sensing and control are improving, incorporating them into a real-world design process is challenging and poses a major obstacle to adoption into everyday technology. Some classes of haptic devices, e.g., grounded force feedback, remain expensive and limited in range. I’ll describe some recent highlights of an ongoing effort to understand how to support haptic designers and end-users. These include a wealth of online experimental design tools, and DIY open sourced hardware and accessible means of creating, for example, expressive physical robot motions and evolve physically sensed expressive tactile languages. Elsewhere, we are establishing the value of haptic force feedback in embodied learning environments, to help kids understand physics and math concepts. This has inspired the invention of a low-cost, handheld and large motion force feedback device that can be used in online environments or collaborative scenarios, and could be suitable for K-12 school contexts; this is ongoing research with innovative education and technological elements. All our work is available online, where possible as web tools, and we plan to push our research into a broader openhaptics effort.
Organizers: Katherine Kuchenbecker
Why cannot the current robots act intelligently in the real-world environment? A major challenge lies in the lack of adequate tactile sensing technologies. Robots need tactile sensing to understand the physical environment, and detect the contact states during manipulation. Progress requires advances in the sensing hardware, but also advances in the software that can exploit the tactile signals. We developed a high-resolution tactile sensor, GelSight, which measures the geometry and traction field of the contact surface. For interpreting the high-resolution tactile signal, we utilize both traditional statistical models and deep neural networks. I will describe my research on both exploration and manipulation. For exploration, I use active touch to estimate the physical properties of the objects. The work has included learning the hardness of artificial objects, as well as estimating the general properties of natural objects via autonomous tactile exploration. For manipulation, I study the robot’s ability to detect slip or incipient slip with tactile sensing during grasping. The research helps robots to better understand and flexibly interact with the physical world.
Organizers: Katherine Kuchenbecker
Gliding evolved at least nine times in mammals. Despite the abundance and diversity of gliding mammals, little is known about their convergent morphology and mechanisms of aerodynamic control. Many gliding animals are capable of impressive and agile aerial behaviors and their flight performance depends on the aerodynamic forces resulting from airflow interacting with a flexible, membranous wing (patagium). Although the mechanisms that gliders use to control dynamic flight are poorly understood, the shape of the gliding membrane (e.g., angle of attack, camber) is likely a primary factor governing the control of the interaction between aerodynamic forces and the animal’s body. Data from field studies of gliding behavior, lab experiments examining membrane shape changes during glides and morphological and materials testing data of gliding membranes will be presented that can aid our understanding of the mechanisms gliding mammals use to control their membranous wings and potentially provide insights into the design of man-made flexible wings.
Modern technology allows us to collect, process, and share more data than ever before. This data revolution opens up new ways to design control and learning algorithms, which will form the algorithmic foundation for future intelligent systems that shall act autonomously in the physical world. Starting from a discussion of the special challenges when combining machine learning and control, I will present some of our recent research in this exciting area. Using the example of the Apollo robot learning to balance a stick in its hand, I will explain how intelligent agents can learn new behavior from just a few experimental trails. I will also discuss the need for theoretical guarantees in learning-based control, and how we can obtain them by combining learning and control theory.
In 1995 Fraunhofer IPA embarked on a mission towards designing a personal robot assistant for everyday tasks. In the following years Care-O-bot developed into a long-term experiment for exploring and demonstrating new robot technologies and future product visions. The recent fourth generation of the Care-O-bot, introduced in 2014 aimed at designing an integrated system which addressed a number of innovations such as modularity, “low-cost” by making use of new manufacturing processes, and advanced human-user interaction. Some 15 systems were built and the intellectual property (IP) generated by over 20 years of research was recently licensed to a start-up. The presentation will review the path from an experimental platform for building up expertise in various robotic disciplines to recent pilot applications based on the now commercial Care-O-bot hardware.
With the ubiquity of catalyzed reactions in manufacturing, the emergence of the device laden internet of things, and global challenges with respect to water and energy, it has never been more important to understand atomic interactions in the functional materials that can provide solutions in these spaces.
During the last three decades computer graphics established itself as a core discipline within computer science and information technology. Two decades ago, most digital content was textual. Today it has expanded to include audio, images, video, and a variety of graphical representations. New and emerging technologies such as multimedia, social networks, digital television, digital photography and the rapid development of new sensing devices, telecommunication and telepresence, virtual reality, or 3D-internet further indicate the potential of computer graphics in the years to come. Typical for the field is the coincidence of very large data sets with the demand for fast, and possibly interactive, high quality visual feedback. Furthermore, the user should be able to interact with the environment in a natural and intuitive way. In order to address the challenges mentioned above, a new and more integrated scientific view of computer graphics is required. In contrast to the classical approach to computer graphics which takes as input a scene model -- consisting of a set of light sources, a set of objects (specified by their shape and material properties), and a camera -- and uses simulation to compute an image, we like to take the more integrated view of `3D Image Analysis and Synthesis’ for our research. We consider the whole pipeline from data acquisition, over data processing to rendering in our work. In our opinion, this point of view is necessary in order to exploit the capabilities and perspectives of modern hardware, both on the input (sensors, scanners, digital photography, digital video) and output (graphics hardware, multiple platforms) side. Our vision and long term goal is the development of methods and tools to efficiently handle the huge amount of data during the acquisition process, to extract structure and meaning from the abundance of digital data, and to turn this into graphical representations that facilitate further processing, rendering, and interaction. In this presentation I will highlight some of our ongoing research by means of examples. Topics covered include 3D reconstruction and digital geometry processing, shape analysis and shape design, motion and performance capture, and 3D video processing.
Learnable representations, and deep convolutional neural networks (CNNs) in particular, have become the preferred way of extracting visual features for image understanding tasks, from object recognition to semantic segmentation. In this talk I will discuss several recent advances in deep representations for computer vision. After reviewing modern CNN architectures, I will give an example of a state-of-the-art network in text spotting; in particular, I will show that, by using only synthetic data and a sufficiently large deep model, it is possible directly map image regions to English words, a classification problem with 90K classes, obtaining in this manner state-of-the-art performance in text spotting. I will also briefly touch on other applications of deep learning to object recognition and discuss feature universality and transfer learning. In the last part of the talk I will move to the problem of understanding deep networks, which remain largely black boxes, presenting two possible approaches to their analysis. The first one are visualisation techniques that can investigate the information retained and learned by a visual representation. The second one is a method that allows exploring how representation capture geometric notions such as image transformations, and to find whether different representations are related and how.
Recent progress in computer-based visual recognition heavily relies on machine learning methods trained using large scale annotated datasets. While such data has made advances in model design and evaluation possible, it does not necessarily provide insights or constraints into those intermediate levels of computation, or deep structure, perceived as ultimately necessary in order to design reliable computer vision systems. This is noticeable in the accuracy of state of the art systems trained with such annotations, which still lag behind human performance in similar tasks. Nor does the existing data makes it immediately possible to exploit insights from a working system - the human eye - to derive potentially better features, models or algorithms. In this talk I will present a mix of perceptual and computational insights resulted from the analysis of large-scale human eye movement and 3d body motion capture datasets, collected in the context of visual recognition tasks (Human3.6M available at http://vision.imar.ro/human3.6m/, and Actions in the Eye available at http://vision.imar.ro/eyetracking/). I will show that attention models (fixation detectors, scan-paths estimators, weakly supervised object detector response functions and search strategies) can be learned from human eye movement data, and can produce state of the art results when used in end-to-end automatic visual recognition systems. I will also describe recent work in large-scale human pose estimation, showing the feasibility of pixel-level body part labeling from RGB, and towards promising 2D and 3D human pose estimation results in monocular images.In this context, I will discuss perceptual, perhaps surprising recent quantitative experiments, revealing that humans may not be significantly better than computers at perceiving 3D articulated poses from monocular images. Such findings may challenge established definitions of computer vision `tasks' and their expected levels of performance.
Organizers: Ludovic Righetti
In the age of large streaming data it seems appropriate to revisit the foundations of what we think of as data modelling. In this talk I'll argue that traditional statistical approaches based on parametric models and i.i.d. assumptions are inappropriate for the type of large scale machine learning we need to do in the age of massive streaming data sets. Particularly when we realise that regardless of the size of data we have, it pales in comparison to the data we could have. This is the domain of massively missing data. I'll be arguing for flexible non-parametric models as the answer. This presents a particular challenge, non parametric models require data storage of the entire data set, which presents problems for massive, streaming data. I will present a potential solution, but perhaps end with more questions than we started with.
Organizers: Jane Walters
The breast is not just a protruding gland situated on the front of the thorax in female bodies: behind biology lies an intricate symbolism that has taken various and often contradictory meanings. We begin our journey looking at pre-historic artifacts that revered the breast as the ultimate symbol of life; we then transition to the rich iconographical tradition centering on the so-called Virgo Lactans when the breast became a metaphor of nourishment for the entire Christian community. Next, we look at how artists have eroticized the breast in portraits of fifteenth-century French courtesans and how enlightenment philosophers and revolutionary events have transformed it into a symbol of the national community. Lastly, we analyze how contemporary society has medicalized the breast through cosmetic surgery and discourses around breast cancer, and has objectified it by making the breast a constant presence in advertisement and magazine covers. Through twenty-five centuries of representations, I will talk about how the breast has been coded as both "good" and "bad," sacred and erotic, life-giving and life-destroying.
Autonomous micro aerial robots can operate in three-dimensional, indoor and outdoor environments, and have applications to search and rescue, first response and precision farming. I will describe the challenges in developing small, agile robots and the algorithmic challenges in the areas of (a) control and planning, (b) state estimation and mapping, and (c) coordinating large teams of robots.
It is a great pleasure to invite you to the talk of Ioannis Havoutis (cf. info below) on Monday March 9th at 11h in the AMD seminar room (TTR building, first floor). have a nice week-end, ludovic Quadrupedal animals move with skill, grace and agility. Quadrupedal robots have made tremendous progress in the last few years. In this talk I will give an overview of our work with the Hydraulic Quadruped -HyQ- and present our latest framework for perception, planning and control of quadrupedal locomotion in challenging environments. In addition, I will give a short preview of our work on optimization of dynamic motions, and our future goals.
Organizers: Ludovic Righetti
How is it that biological systems can be so imprecise, so ad hoc, and so inefficient, yet accomplish (seemingly) simple tasks that still elude state-of-the-art artificial systems? In this context, I will introduce some of the themes central to CMU's new BrainHub Initiative by discussing: (1) The complexity and challenges of studying the mind and brain; (2) How the study of the mind and brain may benefit from considering contemporary artificial systems; (3) Why studying the mind and brain might be interesting (and possibly useful) to computer scientists.
In this talk I will give an overview of work I have done over the years exploring physically based simulation of contact, deformation, and articulated structures where there are trade-offs between computational speed and physical fidelity that can be made. I will also discuss examples that mix data-driven and physically based approaches in animation and control.
Paul Kry is an associate professor in the School of Computer Science at McGill University. He has a BMath from University of Waterloo, and MSc and PhD from University of British Columbia. His research focuses on physically based simulation, motion capture, and control of character animation.