[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3306307.3328208acmconferencesArticle/Chapter ViewAbstractPublication PagessiggraphConference Proceedingsconference-collections
invited-talk

Boosting VFX production with deep learning

Published: 28 July 2019 Publication History

Abstract

Machine learning techniques are not often associated with artistic work such as visual effects production. Nevertheless, these techniques can save a lot of time for artists when used in the right context. In recent years, deep learning techniques have become a widely used tool with powerful frameworks that can be employed in a production environment. We present two deep learning solutions that were integrated into our production pipeline and used in current productions. One method generates high quality images from a compressed video file that contains various compression artifacts. The other quickly locates slates and color charts used for grading in a large set of images. We discuss these particular solutions in the context of previous work, as well as the challenges of integrating a deep learning solution within a VFX production pipeline, from concept to implementation.

Reference

[1]
Ryan Baumann. 2015. Automatic ColorChecker Detection, a Survey. (2015). https://ryanfb.github.io/etc/2015/07/08/automatic_colorchecker_detection.html

Cited By

View all
  • (2023)Jigsaw: Graphical Representation for Big Data Management in Digital Film ProductionACM SIGGRAPH 2023 Talks10.1145/3587421.3595444(1-2)Online publication date: 6-Aug-2023
  • (2020)SAUCE: Asset Libraries of the FutureProceedings of the 2020 Digital Production Symposium10.1145/3403736.3403941(1-5)Online publication date: 11-Aug-2020

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGGRAPH '19: ACM SIGGRAPH 2019 Talks
July 2019
143 pages
ISBN:9781450363174
DOI:10.1145/3306307
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 July 2019

Check for updates

Author Tags

  1. compression artifacts
  2. deep learning
  3. machine learning

Qualifiers

  • Invited-talk

Conference

SIGGRAPH '19
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)23
  • Downloads (Last 6 weeks)1
Reflects downloads up to 03 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Jigsaw: Graphical Representation for Big Data Management in Digital Film ProductionACM SIGGRAPH 2023 Talks10.1145/3587421.3595444(1-2)Online publication date: 6-Aug-2023
  • (2020)SAUCE: Asset Libraries of the FutureProceedings of the 2020 Digital Production Symposium10.1145/3403736.3403941(1-5)Online publication date: 11-Aug-2020

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media