Morphology-agnostic humanoid retargeting via perception-motivated graph similarity.

Abstract

Quantifying motion similarity, despite its inherently subjective nature, is a foundational problem for humanoid motion transfer and control across different embodiments. Drawing from cognitive studies revealing that human motion similarity judgments are strongly influenced by spatial relationships among body parts and proximal contacts, we develop a graph-based representation that effectively encapsulates both features. This representation enables the definition of a robust similarity metric through graph distance computations. The proposed metric emphasizes spatial, especially proximal, relationships between body parts, facilitating motion retargeting that preserves these perceptually motivated relational cues across humanoid embodiments with shared semantic body parts and varying Degree of Freedom (DoF) configurations and body proportions. For quantitative retargeting evaluation, we introduce an order-preserving spatial similarity metric that measures how consistently inter-joint distance rankings are preserved between source and target motions.