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Precomputing Avatar Behavior From Human Motion Data. Jehee Lee and Kang Hoon Lee Symposium on Computer Animation ’ 04 Date: 12/5/2006 Reporter: 彭任右. Outline. Introduction State-Action Model Precomputing Synthesis Experiments Discussion. Goal. Pre-computation Interactive Control
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Precomputing Avatar Behavior From Human Motion Data Jehee Lee and Kang Hoon Lee Symposium on Computer Animation’ 04 Date: 12/5/2006 Reporter: 彭任右
Outline • Introduction • State-Action Model • Precomputing • Synthesis • Experiments • Discussion Precomputing Avatar Behavior From Human Motion Data
Goal • Pre-computation • Interactive Control • Motion Data • Minimum Runtime Cost Precomputing Avatar Behavior From Human Motion Data
Introduction • Preprocessing • Motion capture data • Lookup table • Input • Avatar State • Target State • Output • A sequence of action to the goal Precomputing Avatar Behavior From Human Motion Data
State-Action Model Precomputing Avatar Behavior From Human Motion Data
State-Action Model • Avatar State • S • Action • A • Target State • E • State-action Pair • {(S,E), A} Precomputing Avatar Behavior From Human Motion Data
State Space Precomputing Avatar Behavior From Human Motion Data
Precomputing • Pre-compute which action to take at any situation • Reinforcement learning • Trial-and-error • Discover which actions tend to increase the long-run sum of rewards in future trials Precomputing Avatar Behavior From Human Motion Data
Reward • Specific to a behavior • Approach the target • Throw punches at the target • Dynamic Programming • Randomly select one state pair • Select the action that gains the highest reward in one step • Value iteration Precomputing Avatar Behavior From Human Motion Data
Synthesis • Optimal • Select the action with the highest value • Multiple • Select an action with maximum weighted sum • Random • Identify a small number of preferable action • Select one randomly Precomputing Avatar Behavior From Human Motion Data
Experiments • Pentium 4 2.4GHz • 1GB memory • Vicon 120 FPS -> 15 FPS • 8 minutes shadowboxing • 20 different combination of punches • 5 * 5 capture region Precomputing Avatar Behavior From Human Motion Data
Data Annotation • Contact • If ankle or toe is close to the ground and its velocity is below some threshold • Effective Hitting • The magnitude of the fist’s velocity projected onto the forearm axis is above some threshold • 788 effective hitting points Precomputing Avatar Behavior From Human Motion Data
Graph Construction • Transition • If poses at frame i and frame j-1 are similar • Both left foot are about to leave the ground • Strongly Connected Component • 437 states • 27072 transitions Precomputing Avatar Behavior From Human Motion Data
Performance • Preprocessing • Approach • 1 hour • Throw Punches • 7 hours • Run Time • 30 animated boxers sparring • 9 second to create 1000 frames with video and sound disabled Precomputing Avatar Behavior From Human Motion Data
Results Precomputing Avatar Behavior From Human Motion Data
Discussion • Find the optimal behavior • Better than on-line search algorithm • Difficult to coordinate multiple goals • Memory intensive • Apply to other human motion • Collision detection and response Precomputing Avatar Behavior From Human Motion Data