“From VFX Project Management to Predictive Forecasting”

  • ©Hannes Ricklefs, Stefan Puschendorf, Sandilya Bhamidipati, Brian Eriksson, and Akshay Pushparaja

Conference:


Type:


Entry Number: 06

Title:

    From VFX Project Management to Predictive Forecasting

Presenter(s)/Author(s):



Abstract:


    VFX production companies are currently challenged by the increasing complexity of visual effects shots combined with constant schedule demands. The ability to execute in an efficient and cost-effective manner requires extensive coordination between different sites, different departments, and different artists. This coordination demands data-intensive analysis of VFX workflows beyond standard project management practices and existing tools. In this paper, we propose a novel solution centered around a general evaluation data model and APIs that convert production data (job/scene/shot/schedule/task) to business intelligence insights enabling performance analytics and generation of data summarization for process controlling. These analytics provide an impact measuring framework for analyzing performance over time, with the introduction of new production technologies, and across separate jobs. Finally, we show how the historical production data can be used to create predictive analytics for the accurate forecasting of future VFX production process performance.

References:


    Jeffrey Dean and Sanjay Ghemawat. 2008. MapReduce: Simplified Data Processing on Large Clusters. Commun. ACM 51, 1 (jan 2008), 107–113.
    Jerome Friedman, Trevor Hastie, and Robert Tibshirani. 2001. The Elements of Statistical Learning. Vol. 1. Springer Series in Statistics.
    Inc. MongoDB. 2017. MongoDB. http://www.mongodb.com/.
    F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825–2830.
    Hannes Ricklefs. 2013. Pronto: Scheduling the Un-schedulable. In ACM SIGGRAPH 2013 Talks (SIGGRAPH ’13). ACM, New York, NY, USA, Article 29, 1 pages.

Keyword(s):



PDF:



ACM Digital Library Publication:



Overview Page: