Skinned Motion Retargeting with Spatially Adaptive Interaction Guidance

2026-05-19

AI Summary

This work addresses the challenge of preserving interaction semantics—such as self-contacts and proximal distances—in cross-body motion retargeting. The authors propose a geometry-aware motion retargeting framework that employs spatially adaptive anchors to achieve proximity-aware matching. These anchors are dynamically adjusted via differentiable soft projection combined with a Transformer-based optimization strategy to align with the target character’s reachable regions. To maintain the spatial structure of the source motion, a graph autoencoder is integrated into the pipeline. An alternating training scheme jointly optimizes anchor adaptation and motion generation. Experiments demonstrate that the proposed method significantly outperforms existing approaches across diverse body geometries, achieving markedly improved fidelity in interaction preservation.

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.

Problem

• motion retargeting
• interaction semantics
• body shape variation
• spatial proximity
• character animation

Innovation

• spatially adaptive anchors
• geometry-aware motion retargeting
• interaction semantics preservation
• Transformer-based anchor refinement
• differentiable soft projection