Jason Lawrence
I am a research scientist at Google in Seattle, working at the intersection of 3d computer vision, machine learning, and computer graphics. I co-founded and currently lead the research and engineering team behind Project Starline (video). A longer bio and more information about my work is available at my personal webpage.
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Project Starline: A high-fidelity telepresence system
Supreeth Achar
Gregory Major Blascovich
Joseph G. Desloge
Tommy Fortes
Eric M. Gomez
Sascha Häberling
Hugues Hoppe
Andy Huibers
Claude Knaus
Brian Kuschak
Ricardo Martin-Brualla
Harris Nover
Andrew Ian Russell
Steven M. Seitz
Kevin Tong
ACM Transactions on Graphics (Proc. SIGGRAPH Asia), vol. 40(6) (2021)
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We present a real-time bidirectional communication system that lets two people, separated by distance, experience a face-to-face conversation as if they were copresent. It is the first telepresence system that is demonstrably better than 2D videoconferencing, as measured using participant ratings (e.g., presence, attentiveness, reaction-gauging, engagement), meeting recall, and observed nonverbal behaviors (e.g., head nods, eyebrow movements). This milestone is reached by maximizing audiovisual fidelity and the sense of copresence in all design elements, including physical layout, lighting, face tracking, multi-view capture, microphone array, multi-stream compression, loudspeaker output, and lenticular display. Our system achieves key 3D audiovisual cues (stereopsis, motion parallax, and spatialized audio) and enables the full range of communication cues (eye contact, hand gestures, and body language), yet does not require special glasses or body-worn microphones/headphones. The system consists of a head-tracked autostereoscopic display, high-resolution 3D capture and rendering subsystems, and network transmission using compressed color and depth video streams. Other contributions include a novel image-based geometry fusion algorithm, free-space dereverberation, and talker localization. (presentation video)
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Image pipelines arise frequently in modern computational photography systems and consist of multiple processing stages where each stage produces an intermediate image that serves as input to a future stage. Inspired by recent work on loop perforation [Sidiroglou-Douskos et al. 2011], this article introduces image perforation, a new optimization technique that allows us to automatically explore the space of performance-accuracy tradeoffs within an image pipeline. Image perforation works by transforming loops over the image at each pipeline stage into coarser loops that effectively “skip” certain samples. These missing samples are reconstructed for later stages using a number of different interpolation strategies that are relatively inexpensive to perform compared to the original cost of computing the sample. We describe a genetic algorithm for automatically exploring the resulting combinatoric search space of which loops to perforate, in what manner, by how much, and using which reconstruction method. We also present a prototype language that implements image perforation along with several other domain-specific optimizations and show results for a number of different image pipelines and inputs. For these cases, image perforation achieves speedups of 2× - 10× with acceptable loss in visual quality and significantly outperforms loop perforation.
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