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ACM SIGGRAPH THESIS FAST FORWARD

Up to 12 candidates share three-minute oral presentations of innovative ideas live at a special session at SIGGRAPH 2020.

Program Details

SHARE YOUR IDEAS

ACM SIGGRAPH is holding its third annual Thesis Fast Forward program at SIGGRAPH 2020 to provide more young presenters with a platform for sharing innovative ideas and gaining valuable exposure.

The central element of the submission will be a three-minute video presentation by the candidate and an abstract, explaining the central theme of their thesis. The intent is to make the presentation accessible to a non-expert audience, representative of the typical cross-section of SIGGRAPH conference attendees.

Based on the video submissions and, as a secondary criterion on the provided abstracts, a jury will select up to 12 candidates who will be asked to perform three-minute oral presentations live at a special session at SIGGRAPH 2020. A panel of experts will provide immediate commentary after each live presentation and select a best performance. The live presentations will be judged solely on the content of the live three-minute presentation.

The core component of the submission is a 3 minutes video presentation. In this video, the applicants should summarize the key components of their thesis, its merit and potential impact. The video presentation should be picture-in-picture format where the presenter must be visibly narrating. The submission video should be provided via a web link (a link to a video on media sharing website such as YouTube is recommended to avoid encoding issues, but a direct URL to a video file is also acceptable).

2020 ACM SIGGRAPH Thesis Fast Forward Finalists

LIFEBRUSH: AN ILLUSTRATIVE SIMULATION CANVAS FOR THE BIOLOGICAL MESOSCALE
Timothy Davison, University of Calgary

WORKFLOW‐ASSISTED DIGITAL SCULPTING
Mengqi Peng , University of Hong Kong

COMPUTATIONAL DESIGN OF CURVED THIN SHELLS: FROM GLASS FAÇADES TO PROGRAMMABLE MATTER
Ruslan Guseinov, IST Austria

FINE FEATURE RECONSTRUCTION IN POINT SET SURFACES USING DEEP LEARNING
Prashant Raina, Concordia University

SPECTRALLY‐PROGRAMMABLE CAMERAS FOR IMAGING AND INFERENCE
Vishwanath Saragadam, Carnegie Mellon University

EMBODIED ALIEN MOTION IN VIRTUAL REALITY
Agata Marta Soccini, Universita degli Studi di Torino

COMPUTATIONAL ASSEMBLY: FROM SEARCH TO GRAPH LEARNING
Hao Xu, The Chinese University of Hong Kong

APPEARANCE PRESERVING PREFILTERING FOR RENDERING COMPLEX SCENES
Lifan Wu, University of California, San Diego

FUNCTIONAL COMPUTATIONAL DESIGN
Ran Zhang, IST Austria

COMPUTATIONAL SINGLE‐PHOTON IMAGING
David B. Lindell, Stanford University

PROCEDURALLY GENERATED AUDIO FOR SOFT BODY INTERACTIONS
Feng Su, Carleton University

A GENERATIVE MODELING FRAMEWORK FOR THE INTUITIVE SPATIAL MANIPULATION OF ARBITRARY IMAGE CONTENT
Kyle Olszewski, USC

DEEP LEARNING FOR CHARACTER ANIMATION AND CONTROL
Sebastien Starke, University of Edinburgh

 

Thesis Fast Forward Committee

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Eftychios Sifakis University of Wisconsin-Madison
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M. Alex. O. Vasilescu University of California, Los Angeles