Skel-inbetweening for Intuitive Neural Motion Authoring

In this paper, we introduce a Neural Motion Rig called SKEL-Betweener, tailored to interactive motion authoring. SKEL-Betweener is able to generate long motion sequences from two poses only, and enables intermediate motion authoring via neural motion curves—intuitive joint-level controls for positions and orientations.

November 11, 2024
ACM SIGGRAPH Asia (2024)

 

Authors

Dhruv Agrawal (DisneyResearch|Studios/ETH Joint PhD)

Jakcob Buhmann (DisneyResearch|Studios)

Dominik Borer (DisneyResearch|Studios)

Robert W. Sumner (DisneyResearch|Studios/ETH Zurich)

Martin Guay (DisneyResearch|Studios)

Skel-inbetweening for Intuitive Neural Motion Authoring

Abstract

Authoring 3D motions is a laborious process that requires manipulating and coordinating many control handles over time. Neural motion representations learned from large motion datasets have recently shown impressive capabilities in many motion completion tasks. However, current methods are not designed for interactive motion authoring workflows. The reasons being their requirement of a dense context of full poses, which takes considerable time to author, as well as their lack of joint-level controls for refinement. In this paper, we introduce a Neural Motion Rig called SKEL-Betweener, tailored to interactive motion authoring. SKEL-Betweener is able to generate long motion sequences from two poses only, and enables intermediate motion authoring via neural motion curves—intuitive joint-level controls for positions and orientations. Through user evaluations, we demonstrate the effectiveness of our Neural Motion Rig for efficiently creating and editing motions.

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