Lessons and Insights from Creating a Synthetic Optical Flow Benchmark

Jonas Wulff¹, Daniel J. Butler², Garrett B. Stanley³, and Michael J. Black¹

¹Max-Planck Institute for Intelligent Systems, Tübingen, Germany
jonas.wulff@tuebingen.mpg.de
black@tuebingen.mpg.de

²University of Washington, Seattle, WA, USA
djbutler@cs.washington.edu

³Georgia Institute of Technology, Atlanta, GA, USA
garrett.stanley@bme.gatech.edu

Abstract. With the MPI-Sintel Flow dataset, we introduce a naturalistic dataset for optical flow evaluation derived from the open source CGI movie Sintel. In contrast to the well-known Middlebury dataset, the MPI-Sintel Flow dataset contains longer and more varied sequences with image degradations such as motion blur, defocus blur, and atmospheric effects. Animators use a variety of techniques that produce pleasing images but make the raw animation data inappropriate for computer vision applications if used “out of the box”. Several changes to the rendering software and animation files were necessary in order to produce data for flow evaluation and similar changes are likely for future efforts to construct a scientific dataset from an animated film. Here we distill our experience with Sintel into a set of best practices for using computer animation to generate scientific data for vision research.

LNCS 7584, p. 168 ff.

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