Priama syntéza pohybu s použitím sekvenčného Monte Carlo

Prezentácia vedeckej práceRastislav Hekel, Martin Slavkovský

28.10.2015

Online Motion Synthesis Using Sequential Monte Carlo

Perttu Hamalainen, Sebastian Eriksson, Esa Tanskanen, Ville Kyrki,

Jaakko Lehtinen

Introduction

• Model-Predictive Control (MPC) system for online synthesis of interactive and physically valid character motion

• 3D human character model can balance, dodge projectiles and return to its given pose

• The character can improvise a get up strategy after it has been forced to fall

Example of the Synthesized Motion

Overview

• System generates trajectories of character control parameters for the near future using Sequential Monte Carlo Sampling

• Samples are generated by multimodal, tree-based sampler

• Each sample is evaluated by fitness function• Best sample is the control strategy for the current

frame• Maintaining multiple strategies is crucial for

adaptation to dynamically changing environments

Goals

• Time-varying control strategy that drives the character towards the specified goals, while accounting for changes in the environment

• Motion should be creative and natural with minimal input data

• System should operate at an interactive frame rate at design time, enabling rapid iteration of the goals and constraints

Comparison to Related Works

• Work focuses on optimization based animation of active characters instead of passive ragdolls

• Problem is solved online – offline systems exist for more complex motions

• System does not require handcrafted state machine or dataset of reference motions

• Longer planning horizon (4s), multimodal fitness function, more complex character

Contributions

• The introduction of SMC to online synthesis of physically valid character motion

• A novel sequential sampling method that allows easy integration of machine learning - the sampler utilizes kD-trees for adaptive sampling

• Online, near-real-time synthesis of complex get up strategies

Sequential Monte Carlo Sampling• Widely used in tracking

problems• Tracked probability density is

approximated by set of samples

• Samples are weighted and resampled

• Heavier samples produce more offspring

• New samples are drawn from proposal densities based on the previous samples

Algorithm Overview

• Set of N samples and their associated fitnesses1. Prune the sample set, keep only the best M2. Draw a set of K new samples by optional heuristics and

machine learning predictions3. Construct a sampling prior based on the M + K samples by

inserting the samples in a kD-tree and constructing an adaptive PDF

4. Until the budget of N samples is reached, draw new samples from use each new sample to adaptively update the prior

5. Pick the best sample and use it for driving the simulation forward for the current time step

2D Example

Adaptive Importance Sampling Using a kD-tree

• The tree adaptively subdivides the parameter space into hypercubes

• Each leaf is an hypercube with volume

• • naive kD-tree sampling can

leads to biases• The solution is to treat the

kD-tree as a mixture of Gaussians – their overlap the neighboring hypercubes

Naive vs Gaussian kD-tree

Testing

Results• The system shows

considerable creativity in adapting to surprising situations and utilizing the environment

• The main drawbacks of the system are that movement is sometimes stiff and has unnecessary joint contortions

Method was tested by1. Throwing spheres at the

character2. Adding sudden impulses to

body parts to disturb balance and throw the character around

3. Triggering simulated explosions that add impulses to all body parts

Conclusions

• Paper has demonstrated that Sequential Monte Carlo (SMC) sampling is a viable approach for online synthesis of complex human motion

• Sequential kD-tree sampler has suprisingly high performance• Sampler is simple enough to be implemented from scratch• Improving performance and controlling the style of synthesized

movement are the two main items for future work• Autors plan to investigate whether sequential sampling is

competitive also in offline synthesis, where the function landscape changes over time when the animator interactively adjusts parameters

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