1. Easy Generation of Facial Animation Using Motion Graphs
J. Serra, O. Cetinaslan, S. Ravikumar, V. Orvalho and D. Cosker
J. Serra, O. Cetinaslan, S. Ravikumar, V. Orvalho and D. Cosker
Presented by
Andreas Kelemen Costas Mavridis

2. Introduction Userful for secondary characters.
Most research has focused on body animation
Facial animation seldom studied.
This method uses motion graphs to achieve unique, on-the-fly motion synthesis, controlled via a small number of parameters.
User specifies thresholds that control compression ratio

3. Related Work
Procedural animations vs performance driven and key-frame techniques
Procedural animation divided into three classes:
Constraint or rule-based
Statistical or knowledge-based
Behavioural-based
Motion graphs:
Fit within statistical procedural animation
Widely used for body animation
Inspired by the motion graphs of Kovar et al.

4. This Paper - Method Two steps:
Create the region graphs from analyzing the DB
Traverse these structures to synthesize the motion using Dijkstra’s algorithm to minimize similarity between source and target node

5. This Paper - Method As a result: Compact DB representation
Faster generation of animations
Authoring of animation with a low number of input parameters.
Motion graphs approach specifically tuned for facial animation
Novel approach to ease the choice of the thresholds
A new method for introducing coherent noise in animation

6. This Paper - Method

7. Motion Graph Generation
Information contained in graph:
Nodes contains average displacement of merged poses
Mitigate errors in alignment, effects of facial proportions
Each edge has a similarity value
Average number of consecutive merged nodes
Number from neutral-to-peak and/or peak-to-peak expressions and respective standard deviation

8. Structure of Motion Training Data

9. Structure of Motion Training Data
Dynamic 2D/3D sparse DB, whose samples need to be aligned
Any sample can then be incorporated into a graph as long as it has
Sparse landmarks
Same equivalent pose (Peq) as all other samples
Has the transitions between peak poses and a peak pose

10. Structure of Motion Training Data
Pose alignment revolves around two steps:
All poses are rigidly aligned with their equivalent pose
The first pose is aligned with the average of all samples (Procrustes analysis) to reduce the effect of different proportions

11. Structure of Motion Training Data
Cohn–Kanade (CK and CK+)
68 landmarks reduced to 23
Poses: happiness, sadness, disgust, surprise, anger, fear, contempt & neutral

12. Structure of Motion Training Data
Pre-processing to reduce error/jitter
Sigmoid fitting procedure
Least squares to find the optimal parameters

13. Graph Creation

14. Similarity metric Spatial location of the landmarks
Instantaneous velocity

15. Optimizing for compression
Specifying the desired compressions and tolerances
The author controls the trade-off between motion quality and flexibility of the graph
Compression is calculated differently for each stage
This method locally optimizes the thresholds for each stage/sequence

17. Motion Synthesis

18. Motion Synthesis
Choosing the nodes relevant to desired facial behaviour
Can directly choose the sources and destinations in all graphs
Can specify the label (emotions)
The actual landmark movements still need to be extracted

21. Motion Synthesis - Reconstructing motion from path
Because of compression information is lost
Each node allows approximation of information lost when the poses were merged.
Using the average of consecutive merged nodes we construct the average displacement landmarks velocity
Use the peak nodes average duration as the sequence length
Using the peaks durations allows more realistic synthesis

22. Motion Synthesis - Reconstructing motion from path
Normalize the displacements of each facial region
The motion as a whole does not become linear, and keeps its core motion properties
Smooth the sequences using the Savitzky–Golay window-based filter and sigmoid fitting
It can approximate linear motions, such as blinking
Removes drastic motions
Removes any left zigzag

23. Motion Synthesis - Introducing variation
Inherently introduce noise, as each facial region has its own motion graph and respective path
More variety of emotions for crowds
Additional variations in the sequences length and the chosen path and relies on the standard deviation
The path variations build on top of Dijkstra’s path
Noise is controlled by the percentage of the original path that can be changed.

24. Results

26. Results
Motion graphs were created and tested with 70 sequences from ∼20 subjects of the CK/CK+
Smaller DB with 6 x < Pneutral Ppeak expression Pneutral > and 3 x <Pneutral Ppeak expression Ppeak expression n Pneutral >
Lower data compression -> better representation of motion, more time

27. Results - Bad Local minima occur when creating/using motion graphs
Stiff motion/less expressive
Limited due to number of poses in the DB
DB was not made for facial animation -> subtle poses
Animations hard to recognize
Relies on well defined peak expressions
More complex motions will require extremely large graph
Merging of different expressions for high compression
No gaze and head movements

28. Results - Good Reduces DB size Minimum loss of information
Low error in landmark positions and velocity
Synthesized motion similar to original even with high compression
Data compression ratio typically between boundaries defined by user input
Structure with one region grows exponentially with number of nodes of the graph
Smooth motion
Less input, no equipment

29. Future Works Gaze with complex emotions
More complex animations i.e. talking
Better results with artistic help (better models, better results)
Better Database (Current, only Neutral-Peak but no peak-to-peak)

30. Questions

31. Discussion

32. Discussion
As is, can this method be used in movies and games, and are the results adequate for secondary characters?
It offers a good alternative, but the method is not ready yet. No speech movement, gaze change. Too generic.

33. Discussion
Referring to future work using more complex animation which technology could be used to improve results? How can this be cost-efficient and usable for better secondary characters?
Speaking movement, although it would be difficult to find decent DB for that.
Gaze and head movement is important.
Biggest range of emotions