Continuous Intermediate Token Learning with Implicit Motion Manifold for Keyframe Based Motion Interpolation
This addresses the challenge of motion interpolation for animation and robotics, though it appears incremental as it builds on transformer-based approaches with a novel two-stage method.
The paper tackles the problem of generating continuous and precise 3D motions from sparse keyframes by proposing a framework that formulates latent motion manifolds with keyframe constraints, resulting in superior interpolation accuracy and high visual similarity to ground truth motions on LaFAN1 and CMU Mocap datasets.
Deriving sophisticated 3D motions from sparse keyframes is a particularly challenging problem, due to continuity and exceptionally skeletal precision. The action features are often derivable accurately from the full series of keyframes, and thus, leveraging the global context with transformers has been a promising data-driven embedding approach. However, existing methods are often with inputs of interpolated intermediate frame for continuity using basic interpolation methods with keyframes, which result in a trivial local minimum during training. In this paper, we propose a novel framework to formulate latent motion manifolds with keyframe-based constraints, from which the continuous nature of intermediate token representations is considered. Particularly, our proposed framework consists of two stages for identifying a latent motion subspace, i.e., a keyframe encoding stage and an intermediate token generation stage, and a subsequent motion synthesis stage to extrapolate and compose motion data from manifolds. Through our extensive experiments conducted on both the LaFAN1 and CMU Mocap datasets, our proposed method demonstrates both superior interpolation accuracy and high visual similarity to ground truth motions.