Skinned Motion Retargeting with Spatially Adaptive Interaction Guidance

TL;DR

This paper introduces a geometry-aware motion retargeting framework that dynamically adapts spatial anchors using a Transformer-based strategy to better preserve interaction semantics across characters with different body shapes.

Contribution

The novel approach employs a dynamic, pose-dependent anchor refinement with differentiable soft projection, improving interaction preservation in motion retargeting.

Findings

Outperforms state-of-the-art in interaction fidelity

Effectively handles exaggerated body proportions

Maintains structural coherence in retargeted motion

Abstract

Retargeting motion across characters with varying body shapes while preserving interaction semantics, such as self-contact and near-body proximity, remains a challenging problem. While recent geometry-aware approaches address this by maintaining spatial relationships between predefined corresponding regions, their reliance on static correspondences often struggles when the target character exhibits exaggerated body proportions. In this paper, we present a geometry-aware motion retargeting framework that preserves interaction semantics by performing proximity matching over spatially adaptive anchors. Unlike prior methods with static anchor definitions, the proposed method dynamically repositions anchors to reachable regions on the target character. This is achieved via a Transformer-based anchor refinement strategy that predicts anchor displacements and constrains the translated anchors to remain on the target character geometry through differentiable soft projection. By incorporating pose-dependent spatial structures from the source character, the adapted anchors provide structurally coherent guidance for interaction-aware retargeting. Conditioned on these anchors, a graph-based autoencoder predicts target skeletal motion that preserves the spatial configuration of the source. To encourage task-aligned optimization between anchor adaptation and motion retargeting, we adopt an alternating training scheme in which each module is optimized in turn. Through extensive evaluations, we demonstrate that our method outperforms state-of-the-art approaches in preserving interaction fidelity across diverse character geometries.