Butler DJ, Wulff J, Stanley GB, Black MJ. A naturalistic open source movie for optical flow evaluation. InEuropean Conference on Computer Vision 2012 Oct 7 (pp. 611-625). Springer, Berlin, Heidelberg.

1. A NATURALISTIC OPEN
SOURCE MOVIE FOR
OPTICAL FLOW
EVALUATION
PRESENTER: ABDULRAHMAN HAJE KARIM

2. ABSTRACT
• a new optical flow data set derived from the open source 3D animated short film Sintel.
• It evaluates recent optical flow algorithms, suggesting that further research on optical
flow estimation is needed.
• It compares the image- and flow-statistics of Sintel to those of real films and show that
they are similar.
• The key advantages are: longer image sequences, ground truth flow for all frames, large
non-rigid motions, more complexity (blur, atmosphere, specular surfaces, etc.), and a
larger training set for machine learning methods. The dataset contains data flow than the
Middlebury dataset.

3. INTRODUCTION
• No sensor provides direct measurements of scene motion, and manual labeling is both
impractical and inaccurate.
• They introduce MPI-Sintel, a new data set for optical flow evaluation that addresses
many of these limitations. This data is derived from the animated short film Sintel. It
contains richly varied motion, illumination, scene structure, material properties,
atmospheric effects, blur, etc.
• One of the key novelties of the MPI-Sintel data set is that they render the same scenes
with different render settings, gradually increasing complexity.

4. INTRODUCTION
• They render a subset of the evaluation sequences with slightly modified camera and
object motion. This yields sequences that appear similar to the original Sintel data but
have optical flow that is not public. A flow algorithm that performs much worse on
perturbed sequences than non-perturbed ones suggests possible fraud.
• Contributions include:
1) the introduction of MPI-Sintel, a new data set
2) an analysis of the statistical properties of the data suggesting it is sufficiently
representative of natural movies to be useful;
3) new evaluation measures;
4) an initial comparison of public-domain flow algorithms;
5) an evaluation website that maintains the current ranking and analysis of methods [8].

5. INTRODUCTION

6. INTRODUCTION

7. PREVIOUS DATA SETS
1) First, good data sets facilitate technological progress in the field and are therefore
worth developing.
2) Second, the lifespan of any data set is limited. At some point it can no longer be used
to differentiate methods because their performance saturates.
3) Third, any data set makes compromises and focuses on a subset of issues in the field.
Middlebury does not contain large motions and motion blur.
4) Fourth, a centralized public comparison is important to fairly summarize the state of
the art and encourage innovation through competition.

8. DESIGN DECISIONS AND COMPARISON TO MIDDLEBURY
• Sequence Length : Middlebury sequences are 8 frames long, with several only being 2 frames
long. Ground truth flow is provided only for one pair of frames in each sequence. In contrast,
most Sintel sequences are 50 frames long with 49 ground truth flow fields.
• Amount of Data. Middlebury was not designed to provide training data for machine learning
methods.
• Image Resolution. Middlebury images range 640 × 480. While, in principle, we can render
Sintel frames at any spatial resolution.
• Large Motions. While Middlebury has some large motions (up to 12 pixels per frame (ppf) in
the real imagery and 35 ppf in the synthetic) most are quite small. While it is well over 100
ppf.
• Blur. Middlebury uses stop-action for the “real” sequences and renders graphics scenes with
no motion or defocus blur. While Sintel has it.

9. DESIGN DECISIONS AND COMPARISON TO MIDDLEBURY
• Motion Boundaries and Occluded Regions. They introduce a novel definition of motion
boundaries and a new error measure that computes flow error as a function of distance
from boundaries.
• Real-World Challenges and Transparency.

10. THE SINTEL DATA SET
• Given Sintel’s graphics elements, their motion, and the camera parameters, one can
compute how every pixel moves from one frame to the next. .They modified Blender’s
internal motion blur pipeline to give accurate motion vectors at each pixel giving the
ground truth optical flow maps.
• In some cases they modified the rendering parameters to produce output that is
appropriate for optical flow evaluation.

11. REFERENCE
• Butler DJ, Wulff J, Stanley GB, Black MJ. A naturalistic open source movie for optical flow
evaluation. InEuropean Conference on Computer Vision 2012 Oct 7 (pp. 611-625).
Springer, Berlin, Heidelberg.