1. BATTLECAM™: A Dynamic Camera System for Real-Time Strategy Games
Yangli Hector Yee
Graphics Programmer, Petroglyph
Elie Arabian
Lead Artist, Petroglyph

2. Overview Background Theory Implementation Hacks Cinematic Shots
Question & Answers

3. Background – History RTS Bird’s Eye (Dune 2)
First Person (Dungeon Keeper)
Scripted Actor & Camera (Warcraft 3)
Unscripted Actor, Unscripted Camera (Star Wars – Empire at War)

4. Background - Problem Make a ‘movie’ from an RTS battle
Actors can move during shot
Actors can die during shot
Players can move actors
Objects can move into camera

5. Background - Solution Pick most interesting object Construct shot
Play shot
Fallback on death
Pick next object
Hijack existing camera scripting

6. Theory – Visual Attention
How to pick ‘interesting object’
Bottom Up: Stimulus
Intensity (black on white)
Motion (moving stuff)
Color (red on green)
Orientation (circle in stripes)
Top Down: Goals
Game Objectives
Current User Selection

7. Theory – Bottom Up Attn. Image Orientation Color Spatial Frequency
Intensity
Motion
Feature Maps
Conspicuity Maps
Saliency Map
Reference :
Itti L, Koch C. “A Saliency-Based Search Mechanism for Overt and Covert Shifts of Visual Attention.” Vision Research, pp. 263, Vol 40( ), 2000

8. Center Surround
Mechanism
Intensity
Feature Maps
Lateral Inhibition
Intensity
Conspicuity Maps

9. Lateral Inhibition
One signal vs similar signals

10. Lateral Inhibition
Purpose : Promote areas with significantly conspicuous features while suppressing those that are non-conspicuous.
(Before Inhibition)
(After Inhibition)

11. Implementation Game Logic Driven Images too expensive
No screen space stuff
Access to game logic info

12. Implementation – Data Game logic data (stimulus)
Size
Attack power
Location
Health
Game logic data (goal driven)
Current selection
Visibility

13. Computing Saliency E.g. Saliency_Speed for object(i)
Saliency_speed(i) = (speed(i) – min_speed)/ (max_speed – min_speed)
Normalized 0 to 1
Three Normalization modes
Large is important
Small is imporant
Closeness to mean is important

14. Normalization Modes Large is important Small is important
Saliency_val(i) = (val(i) – min_val) / (max_val – min_val)
Small is important
Saliency_val(i) = 1 – (val(i) – min_val) / (max_val – min_val)
Close to mean is important
Saliency_val(i) = 1 – (val(i) – avg_val) / (max_val – min_val)

15. Normalization settings
Large is important
Size
Attack power
Targets
Speed
Small is important
Health
Close to mean is important
X, Y coordinate

16. Lateral Inhibition
Conspicuity value = saliency_val * (max_saliency_val – min_saliency_val)
Signals with great difference between max and min get boosted

17. Importance Importance (i) = Sum of conspicuity_vals * weights
Weight values
Size 1.0
Power 1.0
X 0.5
Y 0.5
Health 1.0
Targets 1.5
Speed 1.0
Sort list by importance

18. Summary Compute normalized saliency Perform lateral inhibition
Weighted sum
Sort by importance

19. Picking interesting object
Pick current selected
Pick current object’s target 50% of the time
Make interesting object list
From list pick top 5 randomly. Reject if it was same type as the previous object looked at.

20. Constructing Cinematics
Local Space
Transform object space cinematic into world
Local Space without rotation frame
Use translation only
World space using reference objects
For artist driven cinematic constructed in world space
Transform to local space

21. Local Space Cameras

22. Flyby camera shot

23. Circle camera shot

24. Chase camera shot

25. Hardpoint camera shot

26. Frigate/Target camera shot

27. Demo & Q&A
Thanks to
Jim Richmond for camera system
Kevin Prangley for illustrations
Petroglyph staff for support
Contact Info
Hector at petroglyphgames dot com