Virtual Humans in Cultural Heritage discusses adding virtual humans to cultural heritage sites through 3D modeling and animation. It describes creating avatars through 3D modeling pipelines, adding variety through textures and animations, and setting up crowds using brushes. Automatic navigation graphs are generated to guide avatar movement and behavior in smart environments where avatars interact with objects through animated behaviors. The goal is to use virtual humans to provide a more immersive experience for understanding cultural heritage sites.

1. Virtual Humans in Cultural Heritage
Patrick Salamin, Mireille Clavien, Frédéric Vexo, Daniel Thalmann
Patrick Salamin, Ph. D. student
VRLab/EPFL, Switzerland

2. 2
Outline
• Introduction
• The avatars in Cultural Heritage
– Creation of an avatar
– Crowds: requirements and constraints
– Avatars behavior
– Navigation graphs
– Creation of a smart environment
• Conclusion

3. 3
Introduction
• Motivation:
• Adding believable characters to virtual
reconstructions allows non-experts a better
emotional involvement in a virtual reality scene.
• Examples based on european
projects:
Erato, Cahrisma, Epoch, Pompeii

4. 4
Contributions
• Adding variety
– Texture and animation
• Providing tools for crowd setting up
– Brushes
• Automatic navigation graphs
• Interaction with semantic environments

5. 5
Avatar creation
• 3DS Max exporting
– Pipe-line for converting character and animation data
to format usable by crowd rendering and animation
engine
– Exported data:
• Mesh
• Texture
• UV coordinates
• Skeleton hierarchy
• Deformations bindings
• Animations

6. 6
Avatar creation
• Textures design
- Optimize texture mapping: only one material for
each mesh
- => all visual elements
(clothes, skin, face)
are mixed in one
single texture
– Reduce texture size
max 512x512 pixels

7. 7
Avatar creation
• Deformations design
– Adapt skeleton and deformation boxes to each mesh
– Adjust deformations
parameters
– Key-postures to test
deformations

8. 8
Avatar variety 1
• Textures design
– Use same texture mapping for different meshes
– Generate many different characters by varying
colours
7 templates and 15 textures create an infinite variety of virtual romans

9. 9
Avatar variety 2
• Each template has various sets of animation
corresponding to specific emotional states

10. 10
Avatar variety 3
• Variety of walking animations is ensured in realtime
by slight rotation shifting on spine and arms joints
Roman social classes are differentiated through clothes colors and walking style (spine bending)

11. 11
Crowd: requirements & constraints
• Technical challenges: increased demand on
computational resources
– Multi-agent: large number of agents
– Collision avoidance
– Agent-agent interactions
– Interaction with environment
– Interaction with users
– Different from single agent simulations
– Conceptual differences: need for variety

12. 12
Behavior
• Virtual human agent
– 3D graphic body representation
– able to perform low-level actions
(walking with different gaits, playing
animations of gestures, postures,
speak, etc.)
– Has set of internal attributes corresponding
to various psychological, physical or
scenario states (mobility, role,
body size, etc.)

13. 13
Behavior
• Virtual human agent
– Has set of higher level complex
behaviors (wander, follow-path, script,
etc.)
– Has set of rules determining selection
of these behaviors
– Able to receive events from:
• Environment
• Other agents
• User interface

14. 14
Behavior – spray paradigm
• Brushes
– Tools with visual representation on the screen
– Affect crowd members in different manners:
• Create new individuals in the scene
• Change their appearances or behaviors
Negative
Plebeians
Patricians Neutral
Deletion brush
Creation brush Nobles Positive

15. 15
Behavior – manual and automatic

16. 16
Walking – procedural modeling
• Virtual Cultural Heritage
– Main focus on reconstruction of major monuments
– But: complete site models are needed for authentic
simulations.
• Provide environment models at moderate cost.
• Procedural models contain semantic information
inherently (e.g. construction history)
• Credits:
– S. Haegler, P. Mueller,
and Prof. L.v.Gool
at Computer Vision Lab,
ETH Zurich Müller, Vereenooghe, Vergauwen, Van Gool, Waelkens
The Antonine Nymphaeum at Sagalassos, 2004

17. 17
Walking – navigation graph
• Vertices = walkable space
• Edges = Gates
• Navigation Flow = Set of Paths
• [Pettré et al. 06,07]

18. 18
Walking – navigation graph
• Rendered geometry

19. 19
Walking – navigation graph
• Geometry semantics

20. 20
Walking – roman crowd behavior

21. 21
Walking – shops

22. 22
Walking – bakeries

23. 23
Walking – bakeries/shops result

24. 24
Walking – look at

25. 25
Walking – stop look at

26. 26
Walking – look at results

27. 27
Walking – final navigation graph
• Navigation graphs automatically generated
depending on the environment geometry [Pettré et
al. 06,07]

28. 28
Walking - results

29. 29
Smart environment
• Virtual character reacts differently depending on
the environment
• Smart object: both avatar and object interact in the
animation

30. 30
Thanks for your attention!