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Pierre Bénard1,2 Jingwan Lu3 Forrester Cole4
Adam Finkelstein3 Joëlle Thollot1,2
1Grenoble University, LJK 2INRIA 3Princeton University 4MIT CSAIL

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• Image space buffers • Object space line
processing extraction
Comprehensible rendering of 3-D shapes, Real-Time Nonphotorealistic Rendering,
Saito and Takahashi, SIGGRAPH 1990 Markosian et al., SIGGRAPH 1997
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 Simple and fast
 Natural coherence and LoD
 Restricted stylization effects
Implicit Brushes for stylized line-based rendering,
Vergne et al., CGF 2011
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 Wide range of styles
 Computationally expensive
 Complex LoD
 No natural coherence
A Procedural Approach to Style
for NPR Line Drawing from 3D models,
Grabli et al., TOG 2010
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Line texture
l
0 Brush path
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Fixed Line View-Dependent Lines
 Creases  Silhouettes
 Ridges and valleys  Suggestive contours
 Apparent ridges
Parameterization = arc-length Parameterization = ?
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• Flatness
 Remain linear in image space
• Motion coherence
 Evolve according to the motion of the object
• Temporal continuity
 Adapt to the topological events
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 Optimization between
2D and 3D
 Multiple brush strokes
per line
 Dependent on the
input connectivity
Coherent Stylized Silhouettes,
Kalnins et al., SIGGRAPH 2003
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Kalnins et al., Coherent Stylized Silhouettes, SIGGRAPH 2003
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 2D Infinite zoom:
Self-Similar Line
Artmap (SLAM)
 Dependent on the
input connectivity
 One brush stroke
per line
Self-Similar Texture for Coherent Line Stylization,
Bénard et al. NPAR 2010
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Spatio-Temporal Analysis for
 Ease propagation Parameterizing Animated Lines,
of parameterization Buchholz et al., NPAR 2011
with CSS
 Input connectivity
 Optimization over the
Snaxels on a Plane,
entire animation
Kevin Karsch and John C. Hart,  Offline computation
NPAR 2011
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• Image space active contours [Kass et al. 1988]
 Shape
 Topology
 Density
• Brush paths
 Coherent parameterization
 Shape abstraction
• Interactive frame rates
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3D scene Feature samples
Image space
lines extraction
Line drawing
Snakes
Brush Paths
Tracking
Geometry
Advection Relaxation
Parameterization
Vectorization
Stylization
Coverage Connectivity
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• Feature samples extracted in image space
 2D position
 Local tangent
 2D velocity
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3D scene Feature samples
Image space
lines extraction
Line drawing
Snakes
Brush Paths
Tracking
Geometry
Advection Relaxation
Parameterization
Vectorization
Stylization
Coverage Connectivity
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Vectorization Tracking
 Coherence  continuous evolution across frames
 Accuracy  faithfully representation of shape
 Coverage  level of detail
 Simplicity  simple topology
 Length  stylization freedom
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• Color regions tracking
in videos
 Off-line computation
 User corrections
SnakeToonz, Agrawala
NPAR 2002
Keyframe-Based Tracking for
Rotoscoping and Animation,
Agrawala et al. SIGGRAPH 2004
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• Advection + Relaxation
Frame f Frame f+1
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• Reprojection
similar to Bousseau et al. 2007 and Lu et al. 2010
Image f
f+1
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• Minimize the energy:
Internal External
– Continuity Attraction by the
– Smoothness features samples
• Semi-explicit Euler scheme
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• Grow / shrink
 re-sampling (similar to Delingette et al. 2000)
• Contour shrinking
 mass-spring forces between the vertices
 almost constant length if no tangential
external force is applied
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• Advection + Relaxation
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• Local vectorization operators
• Applied sequentially in a greedy fashion
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3D scene Feature samples
Image space
lines extraction
Line drawing
Snakes
Brush Paths
Tracking
Geometry
Advection Relaxation
Parameterization
Vectorization
Stylization
Coverage Connectivity
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• Linear image space parameterization:
with = slope
= phase
= arc-length
• Evolve according to the motion and topology
of the snakes
 parameterization at each vertex
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• Propagation at each vertex
 Parameterization stored between two frames
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• Propagation at each vertex
• Linearization (in the least-square sense)
parameterization
arc-length
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• Propagation at each vertex
• Vectorization events
Linearization
 Split
 Extend propagated directly
 Trim
 Merge: mechanism to avoid parameterization
discontinuities
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• Only if the slope and phase match
• Leveling mechanism
pushes the 2 parameterizations to their mid-value:
with
parameterization
arc-length
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• Segments fitting
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• Arcs fitting
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Input samples Active strokes
Robustness toward noise / outliers
 Temporal smoothing
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• New propagation primitive
 for image space lines
 fully automatic
 running at interactive frame rates
• Temporally coherent basis for complex effects
 Arc-length variations (texture, offsets, tapering)
 Shape abstraction
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Thank you for your attention
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