Structure-Aware Shape Synthesis

We propose a new procedure to guide training of a data-driven shape generative model using a structure-aware loss function. Complex 3D shapes often can be summarized using a coarsely defined structure which is consistent and robust across variety of observations. However, existing synthesis techniques do not account for structure during training, and thus often generate implausible and structurally unrealistic shapes. During training, we enforce structural constraints in order to enforce consistency and structure across the entire manifold. We propose a novel methodology for training 3D generative models that incorporates structural information into an end-to-end training pipeline.Comment: Accepted to 3DV 201

Real Time Virtual Humans

The last few years have seen great maturation in the computation speed and control methods needed to portray 3D virtual humans suitable for real interactive applications. Various dimensions of real-time virtual humans are considered, such as appearance and movement, autonomous action, and skills such as gesture, attention, and locomotion. A virtual human architecture includes low level motor skills, mid-level PaT-Net parallel finite-state machine controller, and a high level conceptual action representation that can be used to drive virtual humans through complex tasks. This structure offers a deep connection between natural language instructions and animation control

Animation Control for Real-Time Virtual Humans

The computation speed and control methods needed to portray 3D virtual humans suitable for interactive applications have improved dramatically in recent years. Real-time virtual humans show increasingly complex features along the dimensions of appearance, function, time, autonomy, and individuality. The virtual human architecture we’ve been developing at the University of Pennsylvania is representative of an emerging generation of such architectures and includes low-level motor skills, a mid-level parallel automata controller, and a high-level conceptual representation for driving virtual humans through complex tasks. The architecture—called Jack— provides a level of abstraction generic enough to encompass natural-language instruction representation as well as direct links from those instructions to animation control

Software framework for geophysical data processing, visualization and code development

IGeoS is an integrated open-source software framework for geophysical data processing under development at the UofS seismology group. Unlike other systems, this processing monitor supports structured multicomponent seismic data streams, multidimensional data traces, and employs a unique backpropagation execution logic. This results in an unusual flexibility of processing, allowing the system to handle nearly any geophysical data. In this project, a modern and feature-rich Graphical User Interface (GUI) was developed for the system, allowing editing and submission of processing flows and interaction with running jobs. Multiple jobs can be executed in a distributed multi-processor networks and controlled from the same GUI. Jobs, in their turn, can also be parallelized to take advantage of parallel processing environments such as local area networks and Beowulf clusters. A 3D/2D interactive display server was created and integrated with the IGeoS geophysical data processing framework. With introduction of this major component, the IGeoS system becomes conceptually complete and potentially bridges the gap between the traditional processing and interpretation software. Finally, in a specialized application, network acquisition and relay components were written allowing IGeoS to be used for real-time applications. The completion of this functionality makes the processing and display capabilities of IGeoS available to multiple streams of seismic data from potentially remote sites. Seismic data can be acquired, transferred to the central server, processed, archived, and events picked and placed in database completely automatically