Controllable Single-shot Animation Blending with Temporal Conditioning

TL;DR

This paper introduces a novel single-shot motion blending framework that uses temporal conditioning and skeleton-aware normalization to enable controllable, smooth, and seamless blending of human motions in animation.

Contribution

It presents the first controllable single-shot motion blending method that allows seamless transitions between motions through temporal conditioning and skeleton-aware normalization.

Findings

Produces plausible and smooth motion blends

Enables controllable and seamless motion transitions

Works across various animation styles and skeletons

Abstract

Training a generative model on a single human skeletal motion sequence without being bound to a specific kinematic tree has drawn significant attention from the animation community. Unlike text-to-motion generation, single-shot models allow animators to controllably generate variations of existing motion patterns without requiring additional data or extensive retraining. However, existing single-shot methods do not explicitly offer a controllable framework for blending two or more motions within a single generative pass. In this paper, we present the first single-shot motion blending framework that enables seamless blending by temporally conditioning the generation process. Our method introduces a skeleton-aware normalization mechanism to guide the transition between motions, allowing smooth, data-driven control over when and how motions blend. We perform extensive quantitative and qualitative evaluations across various animation styles and different kinematic skeletons, demonstrating that our approach produces plausible, smooth, and controllable motion blends in a unified and efficient manner.