Synthesizing Realistic Human Motion by Greg Coombe

Applications Games/Special FX Virtual Environments Ergonomics/Design Military/Medical Simulations

Motivation People are skilled at perceiving the subtle details of human motion. We can identify friends by the style of their walk, even from far away. To be compelling, synthesized humans in computer animations and virtual environments must have realistic motion. from Hodgins

Human Motion Data Three major techniques Advantages of Simulation Motion Capture Animators Advantages of Simulation Greater flexibility Interaction with virtual environment Secondary motion (hair, clothes, etc.)

Controlling Virtual Characters from Badler

Control - Language High-level specifications are often in the form of a language ex: Push Neck joint Nod joint draw_head Lchest joint Lshoulder joint Lelbow joint Lwrist joint draw_arm -1,1,1 scale Rchest joint Rshoulder joint Relbow joint Rwrist joint draw_arm Pop Waist draw_torso Lpelvis joint Lhip joint Lankle joint 1 draw_leg from Jack

Control - Keyframe Keyframing, with physical simulation between frames Knowledge-Driven, Interactive Animation of Human Running, Brunderin, A.

Control - Operators Small programs, called operators, perform simple tasks. Ex. Balance, Lift Leg, Turn Head, etc. Complex, human-like behavior from applying multiple operators. Animators usually connect together operators, and supply transitions

How do you do this? Need: Accurate models Flexible control strategies Mass/inertia derived from biomedical literature DOF for joints based on bone/tendon/ligament structure Flexible control strategies High-level, mathematically accurate (“good math makes good simulations”)

Papers Animating Human Athletics. Hodgins, J. K., Wooten, W. L., Brogan, D. C., O'Brien, J. F. SIGGRAPH '95. Animation of Dynamic Legged Locomotion. Raibert, M., and Hodgins, J. SIGGRAPH `91. Transitions Between Dynamically Simulated Motions: Leaping, Tumbling, Landing, and Balancing. Wooten, W. L., Hodgins, J. K., 1997. Adapting Simulated Behaviors For New Characters. Hodgins, J. K. and Pollard, N. S., SIGGRAPH 1997

Some running movies…

Human Model The human model is a set of rigid links connected by rotary joints. These joints have simplified DOF based on skeletal structure.

Control - Operators The joints are controlled by a simple Finite State Machine.

Finite State Machine Each state has equations governing its behavior. ex. When the foot touches the ground, the desired distance from the hip to the heel is: ts – estimated time to contact Θ – angle of runner on ground lf – length of foot x’, y’ – velocity xd’, yd’ – desired velocity

How are forces applied? Torque forces Constraint forces Each internal joint has a simplified muscle model, a “torque source”. Constraint forces Points of contact (such as feet & ground) are modeled with constraints.

Proportional Derivative Servos Not all behavior encapsulated by these equations. Much of human motion is just trying to compensate for forces. Examples include trying to balance, swinging arms when running, etc. Use proportional-derivative servos to control the other limbs (such as arms and hips). This is like correcting your car direction while driving; first steer towards the lane, then back off as you get closer. Raibert, M., and Hodgins, J., "Animation of Dynamic Legged Locomotion," SIGGRAPH `91

Where did these ideas come from? MIT Leg Lab, Raibert showed robotic running could be accomplished using a few simple, de-coupled control laws.

How does this compare? In the paper, some effort is spent trying to evaluate the correctness of these models. There is a later paper (Judgments of Human Motion with Different Geometric Models, IEEE: Transactions on Visualization and Computer Graphics, 1998) where a more rigorous comparison is performed.

How does this compare? Simulated Runner Real Runner

Some vaulting movies…

Closeup

Transitions These systems work well for simple, repeated motions like running or bicycling. But, equations are pretty specific to these motions. What about more complex behavior? Chain together several simple actions Need to handle transitions between actions

Transitions Wooten, W. L., Hodgins, J. K., 1997. Transitions Between Dynamically Simulated Motions: Leaping, Tumbling, Landing, and Balancing.

Scaling Can we adapt the running simulation to humans of different sizes? Need to scale the control algorithms and forces to account for different limb lengths, masses, and inertial moments. Adapting Simulated Behaviors For New Characters Hodgins, J. K. and Pollard, N. S., SIGGRAPH 1997.

Scaling Two stages: Control system parameters are scaled based on the size and moment of inertia of the dynamic models for the new and the old actors. A subset of the parameters is fine-tuned using a search process based on simulated annealing.

Implementation These videos look good. Can I go implement this myself? Maybe, but “observations of human runners were used to tune the parameters to produce a natural-looking gait.” That is, this requires a lot of hand-tuning.

References Virtual Humans: Behaviors and Physics, Acting and Reacting. Siggraph Course 28, 1998. GVU Animation Lab. http://www.cc.gatech.edu/gvu/animation/ UPenn Center for Human Modeling and Simulation http://hms.upenn.edu/