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ExampleBased Composite Sketching of Human Portraits

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Title: ExampleBased Composite Sketching of Human Portraits


1
Example-Based Composite Sketching of Human
Portraits
  • Hong Chen1,2, Ziqiang Liu1,2, Yingqing Xu1,
    Chuck Rose3,
  • Heung-Yeung Shum1, David Salesin4,5
  • NPAR 2004
  • 1 Microsoft Research , Asia
  • 2 University of California, Los Angeles
  • 3 Microsoft Corporation
  • 4 University of Washington
  • 5 Microsoft Research

2
Result
3
Outline
  • Introduction
  • Related work
  • System framework
  • Composing a face
  • Composing hair
  • Examples
  • Conclusions and future work

4
Introduction
  • An interactive system for generating human
    portrait sketches
  • Input
  • A human face image
  • Output
  • A sketch that exhibits the drawing style of a set
    of training examples provided by an artist
  • Style of Japanese cartooning

5
Introduction (Cont.)
  • Propose a composite sketching approach
  • Decompose the data into components that are
    structurally related to each other.
  • As the eyes or mouth
  • Propose a system which combines two separate but
    similar subsystems.
  • The face subsystem
  • The Hair subsystem

6
Introduction (Cont.)
  • After these have been independently processed,
    they are carefully recomposed to obtain the final
    result.

7
Related Work
  • NPR and digital arts
  • Used to depict facial images with an artistic
    style
  • Digital facial engraving Ostromoukhov, SIGGRAPH
    99 and Caricature generation Brennan, 85
  • Emulate traditional artist tools to assist users
    in drawing pictures with a certain style
  • Rare attempt to generate digital paintings by
    learning from artists

8
System Framework
  • Goal
  • Create a system that could leverage the artists
    skill with a high degree of automation.
  • A training set
  • The gamut of east Asian female faces
  • Divide the portrait system
  • The face subsystem
  • The hair subsystem

9
System Framework (Cont.)
  • The face subsystem
  • Segment into sub-problems for each of the natural
    facial features.
  • i.e. eyes, mouth
  • Global and Local models
  • The hair subsystem
  • Be handled carefully with a structural model and
    a detailed model.
  • These sub-problems are tackled independently

10
Composing a Face
  • Objective
  • Construct a model to take an input image I and
    generate a sketch I that matches the style of
    the training examples.
  • Training Set
  • Model

11
Composing a Face (Cont.)
  • Split into two layers
  • Global
  • capture how the artist places each face element
    in the sketch image.
  • Local
  • mimic how the artist draws each independent
    element locally.

12
Composing a Face (Cont.)
13
Drawing the Facial Component with the Local Model
  • A human face is decomposed semantically into 6
    local components.
  • Left right eyebrows, left right eyes, a nose,
    and a mouth

14
Drawing the Facial Component with the Local Model
(Cont.)
  • Extract the accurate shape and associated texture
    information using an Active Shape Model.
  • Determine to which prototype the component
    belongs
  • Build a different classifier for each type of
    component to cluster the input components into
    the appropriate prototype.
  • KNN interpolation

15
K Nearest Neighbor
  • The goal is to find a class label for the unknown
    example xu

- Euclidean distance - K5
16
Composing the Face Using the Global Model
  • The global model
  • Use to arrange each face element on a canvas.
  • For drawing facial caricatures
  • Relationship of elements to others of their own
    kind.
  • Relationship of elements to their surrounding and
    adjacent elements.

17
Composing the Face Using the Global Model (Cont.)
  • For the representation of Ig

18
Composing the Face Using the Global Model (Cont.)
  • By not tying these relations to fixed value
  • The model can adjust the size of the feature as
    the overall size of the head is changed.
  • Generate Ig
  • Use Active Shape Model
  • 87 control points
  • Determine the placement of the face elements on
    the cartoon canvas.

19
The Placement of the Face Elements on the Cartoon
Canvas
  • Each element needs five parameters
  • (tx, ty), (sx, sy),?

20
Composing Hair
  • Hair cannot be handled in the same way as the
    face
  • Hair has many styles.
  • Is not structured in the same regular way that
    faces are
  • A single unit
  • Render using long stroke
  • There is no clear correspondence between regions
    of two different hairstyles.

21
Hair System Flow
22
Hair System Flow (Cont.)
  • Structural components
  • Coarsely segment the hair into five segments
  • Each indicates important global information about
    the hair.
  • The detail model
  • Add uniqueness and expression to a portrait.

23
Hair Composite Model
  • The global hair structure or impression is more
    important than the detail.

24
Extracting the Image Features for the Hair
  • Determine the image features of the hair
  • Match against the database
  • An estimated alpha mask
  • Hair strand orientation fields

25
Fitting Structural Components
  • For an input image, finding the best training
    data.
  • Classify input image into the correct style.
  • Find the best training example.

26
Fitting Structural Components (Cont.)
  • Deform hair components to a standard shape using
    a multi-level freeform deformation warping
    algorithm.
  • Hair orientation vector
  • G gx1,gy1, gx2,gy2 , , gxn,gyn
  • Alpha value
  • a a1,a2 ,,an
  • E(H1,H2) G1-G2wa1-a2

27
Fitting Structural Components (Cont.)
  • Shape a set of corresponding key points
  • S x1,y1,,xm,ym
  • Find the best matched training example
  • Minimize the distance combining the appearance
    and the shape distance

28
Fitting Detail Components
  • Different kinds of detail require slightly
    different approaches.
  • Boundary and bangs

29
Fitting Detail Components (Cont.)
  • Boundary details
  • The alpha value and orientations for these
    patterns are quite different.

30
Fitting Detail Components (Cont.)
  • Bang detail components
  • Used to detect the bang
  • Segment out the bang regions in the alpha mask
  • The orientation field can be inspected.
  • Determine the length of the bang line

31
Synthesizing the Hair Sketch
  • The stroke in the training samples are all
    divided into two classes.
  • Boundary strokes and streamline strokes
  • Link points
  • The points in the strokes crossing the boundary
    of a structural component
  • Face contour exaggeration
  • Adjust the each part of the inner hair boundary
    according to the corresponding face contour

32
Composing the Structural Components
  • Warp to the target coordinates.
  • Match link points those in the same class.
  • Adjust to the average position and link the
    corresponding strokes.
  • Remove unmatched streamline strokes.

33
Add the Detail Strokes
  • Detail components are connected to strokes
    generated by the component match.
  • Warp to the target coordinates from the global
    phase

34
Examples
  • (b) Result of local model.
  • (c) Result of local model plus global model.
  • (d) Result without local model and global model.

35
Examples (Cont.)
  • Compare the effect of adding detail strokes.
  • (b) Result of composing structural components.
  • (c) Composing with detail components.

36
Examples (Cont.)
  • Combine the face and the hair
  • Neck, shoulder, and clothing are chosen from a
    set of templates supplied by the artist.

37
Examples (Cont.)
38
Conclusions
  • Adapting a global/local hybrid was an effective
    approach for generating face portrait sketches.
  • Application
  • Create a virtual person for cartoon style online
    games or chat environments
  • Create sketched portraits for places where a
    sketch is preferred over a photograph

39
Future Work
  • Limitations and future work
  • Add to the training set
  • Encompass faces of many racial backgrounds
  • Render male images
  • A third subsystem to handle
  • Aging, injury, spectacles, and jewelry
  • A face in profile
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