ABSTRACT
Modern digital film productions rely on huge amounts of supplementary asset data to coordinate the composition of filmed and computer generated content. We present the latest evolution of Jigsaw - DNEG’s flagship software used to organise, process, and distribute a wide range of asset data throughout our pipeline. Jigsaw features an intuitive graphical representation of large data-sets, automatic data classification, easy batch processing, seamless pipeline publishing, smooth interactivity, API extendability, and global multi-user coordination. To achieve this, Jigsaw was designed with optimised resource allocation between local and server processing. Large image data-sets are cached on the server as multiresolution JPEGs to be displayed as small thumbnails if viewed all at once or at higher resolution if viewed in detail. Resource intensive tasks are offloaded to the rendering farm for smooth, continued use. To date, Jigsaw has been used in over 300 DNEG shows to ingest, process, and distribute huge amounts of heterogeneous data. As Jigsaw is interdependent on DNEG’s pipeline it is limited to online use, away from the production set where asset data is captured and prepared. Future developments are focused on solving this limitation.
Supplemental Material
- Simon Pabst et al.2015. Jigsaw: multi-modal big data management in digital film production. SIGGRAPH ’15: ACM SIGGRAPH 2015 Posters (2015). https://doi.org/doi/10.1145/2787626.2792617Google ScholarDigital Library
- Yanir Kleiman et al.2019. Boosting VFX production with deep learning. SIGGRAPH ’19: ACM SIGGRAPH 2019 Talks (2019). https://doi.org/doi/10.1145/3306307.3328208Google ScholarDigital Library
Index Terms
- Jigsaw: Graphical Representation for Big Data Management in Digital Film Production
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