InterFaceRays: Interaction-Oriented Furniture Surface Representation for Human Pose Retargeting

Abstract

Motion retargeting is a well-established technique in computer animation that adapts source motion to fit characters with different sizes, morphologies, or environments. Recent deep learning methods have shown promising results in retargeting character motion. However, retargeting human-object interactions to new environments, especially when furniture shapes differ significantly, remains a challenging problem. In this work, we propose a novel retargeting framework to address this challenge by combining motion generative models with optimization-based pose adaptation. Our framework operates in two stages: first, a key pose generator generates the pose of key joints that preserves the interaction state relative to the new furniture; second, final whole-body pose is determined by accommodating the key joints' poses through optimization. A crucial step in our framework is generating key poses that maintain the interaction state of the source motion. To achieve this, we introduce the Interaction Intensity Weight (IIW) and structural rays, called InterFaceRays, which together capture the interaction intensity between body parts and furniture surfaces. The IIW generator, a trained MoE-based decoder from the conditional variational autoencoder (cVAE) model, infers IIWs for the target furniture based on the source motion's interaction state. Extensive experiments demonstrate that our framework effectively retargets continuous character motion across diverse furniture configurations, with the IIW generator significantly enhancing key pose consistency. This hybrid approach offers a robust solution for motion retargeting across dissimilar furniture environments.