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Motion Grammars for Character Animation

Uses formal grammar rewriting rules to synthesize structured motion sequences obeying behavioral rules for multiple characters.

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Abstract

The behavioral structure of human movements is imposed by multiple sources, such as rules, regulations, choreography, habits, and emotion. Our goal is to identify the behavioral structure in a specific application domain and create a novel sequence of movements that abide by structure‐building rules. To do so, we exploit the ideas from formal language, such as rewriting rules and grammar parsing, and adapted those ideas to synthesize the three‐dimensional animation of multiple characters. The structured motion synthesis using motion grammars is formulated in two layers. The upper layer is a symbolic description that relates the semantics of each individual's movements and the interaction among them. The lower layer provides spatial and temporal contexts to the animation. Our multi‐level MCMC (Markov Chain Monte Carlo) algorithm deals with the syntax, semantics, and spatiotemporal context of human motion to produce highly‐structured, animated scenes. The power and effectiveness of motion grammars are demonstrated in animating basketball games from drawings on a tactic board. Our system allows the user to position players and draw out tactical plans, which are animated automatically in virtual environments with three‐dimensional, full‐body characters.

How to read this

Category: Method: grammar-based structured motion synthesis
Contributions: Adapts formal-language rewriting rules and grammar parsing to synthesize 3D animation of multiple interacting characters
A two-layer formulation separating an upper symbolic layer (semantics and interaction) from a lower spatiotemporal context layer
A multi-level MCMC algorithm handling syntax, semantics, and spatiotemporal context, demonstrated by animating basketball games from tactic-board drawings
Context: Builds on data-driven motion synthesis in the lineage of Kovar et al.'s Motion Graphs, layering formal grammars on top to impose behavioral structure.Builds on: Motion Graphs
Correctness: Demonstrated on the structured domain of basketball tactics; the approach assumes the target behavior can be captured by grammar rules, so it suits rule-governed scenarios more than free-form or unstructured motion.
Clarity: Conceptually approachable via the language analogy, but the two-layer formulation and multi-level MCMC need a careful second pass.
How to read it: First pass for the grammar-as-motion-structure idea and the basketball demo; do a second pass on the two-layer model and the MCMC sampler if you intend to apply it to another structured domain.

Builds on

Motion Graphs 2002

Built upon by

Nothing yet.

Related work

Motion Graphs 2002 / SIGGRAPH
A Deep Learning Framework for Character Motion Synthesis and Editing 2016 / SIGGRAPH
Physically Based Motion Transformation 1999 / SIGGRAPH
Character Motion Synthesis by Topology Coordinates 2009 / CGF

Keywords

motion grammars context-free grammar character animation motion synthesis markov chain monte carlo metropolis-hastings parallel tempering motion graph motion capture parse tree multi-character animation basketball tactics rewriting rules grammar parsing finite state machine motion editing spatiotemporal context

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