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
In this talk I will describe the main types of research questions and neuroimaging tools used in my work in human cognitive neuroscience (with foci in audition and sleep), some of the existing approaches used to analyze our data, and their limitations. I will then discuss the main practical obstacles to applying machine learning methods in our field. Several of my ongoing and planned projects include research questions that could be addressed and perhaps considerably extended using machine learning approaches; I will describe some specific datasets and problems, with the goal of exploring ideas and potentially opportunities for collaboration.
Organizers: Mara Cascianelli
Mechanical removal of blood clots is a promising approach towards the treatment of vascular diseases caused by the pathological clot formation in the circulatory system. These clots can form and travel to deep seated regions in the circulatory system, and result in significant problems as blood flow past the clot is obstructed. A microscopi-cally small helical microrobot offers great promise in the minimally-invasive removal of these clots. These helical microrobots are powered and controlled remotely using externally-applied magnetic fields for motion in two- and three-dimensional spaces. This talk will describe the removal of blood clots in vitro using a helical robot under ultrasound guidance. The talk will briefly introduce the interactions between the helical microrobot and the fibrin network of the blood clots during its removal. It will also introduce the challenges unique to medical imaging at micro-scale, followed by the concepts and theory of the closed-loop motion control using ultrasound feedback. It will then cover the latest experimental results for helical and flagellated microrobots and their biomedical and nanotechnology applications.
Organizers: Metin Sitti
Daniel Renjewski presents research in bipedal gait mechanisms: 'Passive mechanisms for increased power and efficiency in bipedal gait’
Incredible biological capabilities have emerged through evolution. Of special note is the material intelligence that defines the bodies of living things, blurring the line between brain and body. Material robotics research takes the approach of imbuing power, control, sensing, and actuation into all aspects of a (primarily soft) robot body. In this talk, the research topics of material robotics currently underway in the mLab at Oregon State University will be presented. Soft active materials designed and researched in the mLab include liquid metal, biodegradable elastomers, and electroactive fluids. Bioinspired mechanisms include octopus-inspired soft muscles, gecko-inspired adhesives, and snake-like locomotors. Such capabilities, however, introduce new fundamental challenge in making materially-enabled robots. To address these limitation, the mLab is also innovating in techniques to rapidly and scalably manufacture soft materials. Though significant challenges remain to be solved, the development of such soft and materially-enabled components promises to bring robots more and more into our daily lives.
Organizers: Metin Sitti
The definition of art has been debated for more than 1000 years, and continues to be a puzzle. While scientific investigations offer hope of resolving this puzzle, machine learning classifiers that discriminate art from non-art images generally do not provide an explicit definition, and brain imaging and psychological theories are at present too coarse to provide a formal characterization. In this work, rather than approaching the problem using a machine learning approach trained on existing artworks, we hypothesize that art can be defined in terms of preexisting properties of the visual cortex. Specifically, we propose that a broad subset of visual art can be defined as patterns that are exciting to a visual brain. Resting on the finding that artificial neural networks trained on visual tasks can provide predictive models of processing in the visual cortex, our definition is operationalized by using a trained deep net as a surrogate “visual brain”, where “exciting” is defined as the activation energy of particular layers of this net. We find that this definition easily discriminates a variety of art from non-art, and further provides a ranking of art genres that is consistent with our subjective notion of ‘visually exciting’. By applying a deep net visualization technique, we can also validate the definition by generating example images that would be classified as art. The images synthesized under our definition resemble visually exciting art such as Op Art and other human- created artistic patterns.
Organizers: Michael Black
One of the central problems of artificial intelligence is machine perception, i.e., the ability to understand the visual world based on input from sensors such as cameras. In this talk, I will present recent progress with respect to data generation using weak annotations, motion information and synthetic data. I will also discuss our recent results for action recognition, where human tubes and tubelets have shown to be successful. Our tubelets moves away from state-of-the-art frame based approaches and improve classification and localization by relying on joint information from several frames. I also show how to extend this type of method to weakly supervised learning of actions, which allows us to scale to large amounts of data with sparse manual annotation. Furthermore, I discuss several recent extensions, including 3D pose estimation.
Organizers: Ahmed Osman
Actions constitute the way we interact with the world, making motor disabilities such as Parkinson’s disease and stroke devastating. The neurological correlates of the injured brain are challenging to study and correct given the adaptation, redundancy, and distributed nature of our motor system. However, recent studies have used increasingly sophisticated technology to sample from this distributed system, improving our understanding of neural patterns that support movement in healthy brains, or compromise movement in injured brains. One approach to translating these findings to into therapies to restore healthy brain patterns is with closed-loop brain-machine interfaces (BMIs). While closed-loop BMIs have been discussed primarily as assistive technologies the underlying techniques may also be useful for rehabilitation.
Organizers: Katherine Kuchenbecker
The recent and ongoing digital world expansion now allows anyone to have access to a tremendous amount of information. However collecting data is not an end in itself and thus techniques must be designed to gain in-depth knowledge from these large data bases.
Organizers: Mara Cascianelli
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, yet markers are intrusive (especially for smaller animals), and the number and location of the markers must be determined a priori. Here, we present a highly efficient method for markerless tracking based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in a broad collection of experimental settings: mice odor trail-tracking, egg-laying behavior in drosophila, and mouse hand articulation in a skilled forelimb task. For example, during the skilled reaching behavior, individual joints can be automatically tracked (and a confidence score is reported). Remarkably, even when a small number of frames are labeled (≈200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
Organizers: Melanie Feldhofer