Interactive Generative Motion Editing via Scheduled Inpainting

2026-07-31

AI Summary

Existing motion editing methods struggle to simultaneously achieve large-scale structural modifications and faithful preservation of original motion, while generative models often lack interactive editing capabilities. This work proposes scheduled inpainting—a novel approach that dynamically controls the balance between preserving and generating motion in specific spatiotemporal regions during inference of a generative model. For the first time, this method unifies generative motion synthesis with interactive editing. It enables flexible operations such as extension, stitching, and composition while maintaining natural motion quality, and achieves high-precision editing through fine-grained spatiotemporal control. Experiments demonstrate that the proposed method outperforms four baselines across multiple tasks, and both ablation studies and user evaluations confirm its effectiveness and practical utility.

Abstract

Motion editing is central to VFX and game development, where it is used extensively to modify and augment existing movements to conform to new environments or changes in artistic direction. While traditional motion editing can do small modifications, it cannot accommodate larger structural edits, resulting in visual warping artifacts that require authoring new motion. Conversely, recent advances in large-scale generative modeling have unlocked newfound capabilities for authoring entire movements by directly manipulating sparse spatial constraints. While impressive at creating new movements, these methods lack the capability to preserve and edit existing motion interactively. In this work, we introduce scheduled inpainting, a method that enables interactive generative motion editing, a novel paradigm unifying motion synthesis and editing by leveraging generative models. Scheduled inpainting is a simple yet powerful inference-based technique that enables fine-grained spatiotemporal control over the balance between preserving the original motion and generating new content. By building atop generative models that support direct manipulation, our system allows artists to interactively refine existing animations while ensuring results remain natural and consistent with the learned motion distribution. Scheduled inpainting is versatile and supports many editing applications, such as extending, stitching, and compositing different clips. Finally, we extensively validate our approach by comparing with four baselines, conducting ablations of our design, and reporting user feedback.

Problem

• motion editing
• generative modeling
• interactive editing
• spatiotemporal control
• motion synthesis

Innovation

• scheduled inpainting
• interactive motion editing
• generative motion modeling
• spatiotemporal control
• motion synthesis