Interactive Generative Motion Editing via Scheduled Inpainting

Researchers at DisneyResearch|Studios and ETH Zurich introduced scheduled inpainting, an inference-based method enabling interactive generative motion editing that unifies motion synthesis and editing by preserving original motion while generating new content. The technique, detailed in a July 30, 2026 arXiv paper, supports applications such as extending, stitching, and compositing motion clips, and was validated against four baselines with ablations and user feedback.

Interactive Generative Motion Editing via Scheduled Inpainting 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. July 30, 2026 arXiv 2026 Authors Dhruv Agrawal DisneyResearch|Studios/ETH Zurich Dominik Borer DisneyResearch|Studios Luca Vögeli DisneyResearch|Studios Robert W. Sumner DisneyResearch|Studios/ETH Zurich Martin Guay DisneyResearch|Studios Jakob Buhmann DisneyResearch|Studios Interactive Generative Motion Editing via Scheduled Inpainting 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.