제 6 주. 응용 -2: Graphics Artificial Life for Computer Graphics D. Terzopoulos, Communications of the ACM, vol. 42, no. 8, pp. 33~42, 1999 학습목표 Understanding biological and evolutionary models to give life to self-animating graphical characters with bodies, brains, behavior, perception, learning and cognition

개요 4 CG 분야의 발전방향 –Purely geometric models  more elaborate physics-based models –A world of objects of much greater complexity that looks alive 4 Artificial life –Construction of advanced graphics models for image synthesis, animation, multimedia, and virtual reality –Incorporation of state-of-the-art geometric and physics-based modeling techniques –Natural process to simulation Birth and death, growth and development, natural selection, evolution, perception, locomotion, manipulation, adaptive behavior, learning, intelligence –Goal: elf-creating, self-evolving, self-controling, self-animating

Signs of ALife 4 Modeling hierarchy 4 Applications –Motion picture special effects industry Stanley and Stella, Batman Returns, Cliffhanger, Lion King, Hercules, Spawn, Mulan, Bug’s Life, etc –Interactive games Cognitive Behavioral Physical Kinematic Geometric ALife Evolutionary

Applications 4 Artificial plants –L-systems (formal language framework) to grow highly complex and realistic graphics models of plants 4 Artificial evolution –Karl Sims’ evolutionary work for graphics (survival of the fittest) 4 Behavioral modeling and animation –Craig Reynolds’ boids experiment: form of behavioral rules governing interaction of multiple autonomous agents capable of locomotion and perception within a virtual world 4 Artificial animals –Motor control, perception, behavior, learning, cognition 4 Artificial humans –Sensory perception in human modeling, equipping virtual humans with visual, tactile, and auditory sensors  sensory awareness 4 Interactive synthetic characters –Creatures, ALIVE, Swamped

Lifelike Autonomous Agents 4 Artificial Fishes –X. Tu at Univ. of Toronto –Autonomous agent with a deformable body actuated by internal muscles –Eye, brain, motor, perception, behavior, learning centers –Perceptual awareness of the virtual world –Collision avoidance, foraging, preying, schooling, mating –Digitized photographs through computer vision techniques –Lagrangian equations of motion (69 equations per fish) –Primitive, reflexive behavior routines: obstacle avoidance –More sophisticated motivational behavior routines: schooling, mating –Cognitive capacity with action-selection mechanism –SMART Toronto ( –Trimension Reality Theater (

Conclusion 4 Alife’s role in computer graphics –Image synthesis, modeling, animation, interactive games, multimedia, virtual reality 4 Challenge to graphics researchers –Develop and creatively deploy ever more sophisticated graphics models by simulating the mechanisms of life