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Events for May 09, 2023
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Kuldeep Meel (National University of Singapore) - Functional Synthesis: An Ideal Meeting Ground for Formal Methods and Machine Learning
Tue, May 09, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Kuldeep Meel, National University of Singapore
Talk Title: Functional Synthesis: An Ideal Meeting Ground for Formal Methods and Machine Learning
Abstract: Don't we all dream of the perfect assistant whom we can just tell what to do and the assistant can figure out how to accomplish the tasks? Formally, given a specification F(X,Y) over the set of input variables X and output variables Y, we want the assistant, aka functional synthesis engine, to design a function G such that F(X,G(X)) is true. Functional synthesis has been studied for over 150 years, dating back Boole in 1850's and yet scalability remains a core challenge. Motivated by progress in machine learning, we design a new algorithmic framework Manthan, which views functional synthesis as a classification problem, relying on advances in constrained sampling for data generation, and advances in automated reasoning for a novel proof-guided refinement and provable verification. The significant performance improvements call for interesting future work at the intersection of machine learning, constrained sampling, and automated reasoning.
Biography: Kuldeep Meel holds the NUS Presidential Young Professorship in the School of Computing at the National University of Singapore (NUS). His research interests lie at the intersection of Formal Methods and Artificial Intelligence. He is a recipient of the 2022 ACP Early Career Researcher Award, the 2019 NRF Fellowship for AI and was named AI's 10 to Watch by IEEE Intelligent Systems in 2020. His research program's recent recognitions include the CACM Research Highlight Award, 2022 ACM SIGMOD Research Highlight, IJCAI-22 Early Career Spotlight, best paper award nominations at ICCAD-21 and DATE-23.
Host: Mukund Raghothaman
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Angela Inostroza-Hoffman / Computer Science
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PhD Dissertation Defense - Zimo Li
Tue, May 09, 2023 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Dissertation Defense - Zimo Li
Committee Members: Andrew Nealen, Laurent Itti, Stefanos Nikolaidis, Mike Zyda
Title: Human Appearance and Performance Synthesis Using Deep Learnin
Abstract: Synthesis of human performances is a highly sought after technology in the entertainment industry. In this dissertation, we will go over several new deep learning solutions which tackle the problems of human facial and body performance synthesis.
Facial performance synthesis is a complex multistep graphics problem. First, the target performance to be modified must be tracked and captured accurately. Then, based on the desired modification (whether to change the identity, facial expressions, or both), a modified source performance must be synthesized or captured from a different actor. Finally, the original facial performance must be removed and replaced with the synthesized one. This multistep process poses many unique challenges. Using conventional CG tracking and retargeting of expressions from the source to target using a 3D mesh and static texture will give an undesired rubbery skin effect. Furthermore, inaccuracies in the expression tracking of the source performance using a blendshape model will result in the uncanny valley effect in the output performance. It is often necessary to use costly capture methods, such as a Light Stage, to obtain highly accurate 3D captures and dynamic textures of a source performance in order to avoid these pitfalls. Even then, final modified performances are often uncanny.
When dealing with human body to motion synthesis, creating new motions often requires manual artist animations, tracking new motions on an actor, or stitching together subsequences of previous animations. These methods are limited by cost, or are not able to generate appreciably novel motions.
Over the last several years, the advancement of AI based generation techniques have let us address many of these issues. In this thesis, we will go over several novel techniques which reduce the cost (time, money, ease-of-access), and improve the quality of facial reenactment, as well as body motion synthesis, pipelines. The applications of these techniques allow us to tackle new problem settings in an efficient way.
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
Event Link: https://us05web.zoom.us/j/86385849747?pwd=V2lwR2FXekI5WVpNMGU0bWF5clJIQT09
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Innovation For Defense Applications Showcase
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Tue, May 09, 2023 @ 04:30 PM - 06:30 PM
Viterbi Technology Innovation and Entrepreneurship
University Calendar
You are invited to join us for the Innovation For Defense Applications team presentations showcase. This semester we have teams that have worked on various problems sets for their Department of Defense sponsors.
The event will be held on the USC campus at the Ronald Tutor Hall (RTH) in room 526. Doors will open at 4:30 pm and will include light refreshments at the event.
If you can not attend in person, we will also provide a ZOOM link for a virtual option.
RSVP
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Johannah Murray
Event Link: https://forms.gle/EZP7rh2y4uPHMcne7
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