Adding Virtual Characters to the Virtual Worlds. Yiorgos Chrysanthou Department of Computer Science University of Cyprus

Transcription

1 Adding Virtual Characters to the Virtual Worlds Yiorgos Chrysanthou Department of Computer Science University of Cyprus

2 Cities need people However realistic the model is, without people it does not have the same impact 1/7/2009 2

3 Adding virtual characters to cities is hard City model is already large scale with its own complexity Add to that thousands/millions of characters Rendering A human model has complex deformable geometry Behaviour Multiple interactions between static and dynamic entities 1/7/2009 3

4 Rendering Three main approaches Polygonal-based Crowd Rendering Point-based Crowd Rendering Image-based Crowd Rendering

5 Using Polygons A good 3D avatar model uses many triangles as the human body has a complex shape Elixir Studios ~23,500 Tris Elixir Studios Rendering 1,000s individuals require a very large polygonal budget

6 Using Polygons (II) Even simple models are composed by thousands of polygons 2K x 10,000 = 20 Millions ~2K Tris

7 Attempt 1: Careful modeling Skilled 3D artists can produce very low-poly, good looking characters, but there is always a limit to simplification Courtesy of Got3D ~3300 faces ~2800 faces Courtesy of Got3D

8 Even more careful modeling... ~1000 faces Courtesy of Stephanie Noverraz

9 Attempt 2: Using LODS Using LODs for distant individuals Lot of care is needed to avoid introducing artifacts LODs can be hardware-unfriendly Elixir Studios 615 Polys 615 x 10,000 = >6 Millions The polygons count may still be too high

10 Attempt 3: Details recovering Normal maps allow perpixel lighting -> recovery of small details Normal maps are efficient on modern HW, but there is still a performance penalty

11 Polygonal crowd: instancing ATI X800 crowds rendering demo

12 Rendering using points Point-based objects are represented as a dense set of surface point samples which contain colour, depth and normal information They are rendered directly and independently without any knowledge of surface topology

13 Rendering crowds using points(i) Wand and Straßer 2002 Football stadium, 16,416 objects, 105 million triangles, rendering time 373 msec (Hierarchical instantiation) (Test system: 2 GHz PIV,1 GB of RAM, GeForce3)

14 Rendering crowds using points(ii) Wand and Straßer 2002 Replicated poser models, objects, 575 million triangles, rendering time 294 msec (Hierarchical instantiation)

15 Image Based Rendering We have seen a lot of progress in recent years Improved techniques Greatly improved hardware Most scalable systems use Impostors for far Geometry for near 1/7/

16 Impostors Run time Pre-processing Tecchia, Loscos and Chrysanthou, IB Crowd Rendering, IEEE CG&A 2002, GCF /7/

17 Impostors + geometry Combination of techniques Impostors for far Geometry for closer, with either Geometric LODs such as in the Geopostors or Static pre-compiled meshes Dynamic Meshes 1/7/

18 Behavior of the avatars At several levels A. Authoring of the city: distributing the people in a realistic way and maintain their flow in the city for stability B. Ambient crowd C. Interactive-social behaviors 1/7/

19 Distributing the characters How do you place the characters in such a city? More in city centre and central streets Less in out-of-theway areas Place then in an Easy and quick, yet Controlled way 1/7/

20 Interactive Authoring Ulicny, Ciechomski,, Thalmann(2004) Crowdbrush:: Interactive Authoring of Real-time Crowd Scenes

21 Ideas from other disciplines Ledra Bill Hiller, Bartlett School of Architecture Evagorou Makariou Pedestrian activity can be considered the product of two distinct components: The configuration of the street network (space syntax) The location of particular attractors (shops, offices, happenings, etc.) Stylianou, Fyrillas and Chrysanthou, Scalable Pedestrians Simulation, ACM VRST 04 1/7/

22 Pre-processing Negative binomial For each node: how long avatars stay in it cells and portals For each edge: attractiveness space syntax + attractors Iterative diffusion simulation Distribution & flows Stylianou, Fyrillas and Chrysanthou, Scalable Pedestrians Simulation, ACM VRST 04 1/7/

23 Crowd Behavior Yiorgos Chrysanthou (Most slides taken from Alon Lerner)

24 Different Approaches Rule based approaches can produce realistic simulations. Different rules required for different situations. Rules can be local, global, reactive, cognitive... Other approaches tend to capture the flow of a crowd. Usually, do not capture subtleties of individual behaviors. Rule based: Reynolds 87, Terzopoulos et. et. al. al Musse et. et. al. al Funge et. et. al al Loscos et. et. al. al Lamarche et. et. al. al Shao and Terzopoulos Massive Software AI AI Implant Software Fluid Mechanics: Hughes Treuille Particles: Heigeas Social Forces: Helbing 95 95

25 Flocks, Herds and Schools Separation: steer to avoid crowding local flock mates. Alignment: steer towards the avg. heading of local flock mates. Cohesion: steer to move toward the average position of local flock mates REYNOLDS, C. W Computer Graphics 21. Flocks, herds, and schools: A distributed behavioral model.

26 Related Works

27 Global & Local Rules Most of the works give the simulated agents geographic goals to reach. Global path planning is used to guide the agents in the correct direction. Local rules are defined to avoid collisions and simulate behaviors

28 Global & Local Rules Shao w., Terzopoulos D. SCA 2005 Autonomous Pedestrians

29 Treuille A., Cooper S. Popovic Z. Siggraph 2006 Continuum Crowds

30 The Movie Industry AI software is a network of nodes, where each node is a sensor or a rule. Sensors allow an agent to detect information in its immediate surroundings. You create rules based on these (sensors) To have a fully convincing simulation you d require a huge number of sensors and rules... CGSociety interview with VFX supervisor on the film Troy 2005

31 The Movie Industry We found that many of our shots did not need such a high degree of complexity. Clean Plate All we needed were rules that prevented them from bumping into each other! Simple Render Final Render

32 Crowd Characteristics Different behaviors: Stopping / Standing Grouping / Dispersing Changes in direction Slight movements Walking against the flow etc... Most approaches cannot recreate these behaviors.

33 The Basic Idea Real people know how to behave, simulated ones do not. Copy behaviors from real people. Behavior is is the the trajectory over a short period of of time.

34 The Basic Idea If an agent can find a person facing a similar situation, then it can copy its trajectory, thereby mimicking its behavior. The marked people are are facing similar situations.

35 Defining Examples During preprocessing: Key frames are manually track in the video and the people s paths interpolated. An influence function is used to define the configuration of influencing factors for each person at each frame. Examples store the trajectory of a person and its configuration of influencing factors.

36 Influencing Factors An influencing factor can be anything that influences a person s behavior. We consider people and obstacles as influencing factors. Not all factors have the same amount of influence. Potentially Influencing Factors: Personality, Personality, Emotions Emotions Terrain, Terrain, Obstacles Obstacles Surrounding Surrounding people people......

37 The Influence Function A continuous function. Quantifies each factors influence. High Factors in front influence more than those behind. Values below a certain threshold are considered non-influential. Influence is computed over time. Accounts for the relative velocity of the influencing factor. Low

38 A Few Examples

39 Simulation During a simulation an agent: Defines a query. Searches the database for a matching example. Copies the trajectory from the example. Follows the trajectory until a new one is needed. define query find example copy trajectory walk

40 Queries A query is is the the configuration of of influencing factors for for a simulated agent query example

41 Queries To see if an example matches a query, we align them and use a continuous function to determine their similarity. query example

42 Matching Function Validity check: To assure a collision free simulation the example path is checked for collisions against the query s factors. Query Example To assure a smooth transition the velocities of the query and example subjects are checked.

43 Matching Function For each query factor, find the most similar example factor. Quantify similarity in terms of position, velocity and direction over time. Query Example Multiple query factors can be matched to the same example factor. Penalize matching value for unmatched factors. Length of of copied trajectory is is determined by by the the quality of of the the match.

44 Collision Avoiding Paths There are occasions where all matching examples lead to a collision. Query Example Find a collision free example trajectory. Use the matching function to match the historical path of the query and example subjects. historical path

45 Results Tracking times range from several hours to a day. The number of examples generated from a video is in the tens of thousands. We filter similar examples from consecutive frames. The needed storage space is about 100MB. Crowd density and length of of video determines tracking times and number of of examples created.

46 Results 2 minute simulation 3000 simulated frames Sparse crowd input 20 agent average per frame ~10 minute computation. Dense crowd input 40 agent average per frame ~1 hour computation.

47 Discussion The running time of the simulation is affected by: The similarity between the example crowd and the simulated crowd. The variety of examples that exist in the database. The presence of obstacles. These factors affect the the number of of queries needed.

48 A Simulated Crowd A collision free simulation whose agents exhibit a variety complex group and individual behaviors.

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