Human-in-the-Loop Local Corrections of 3D Scene Layouts via Infilling

Christopher Xie; Armen Avetisyan; Henry Howard-Jenkins; Yawar Siddiqui; Julian Straub; Richard Newcombe; Vasileios Balntas; Jakob Engel

arXiv · 2503.11806

Human-in-the-Loop Local Corrections of 3D Scene Layouts via Infilling

Christopher Xie·Armen Avetisyan·Henry Howard-Jenkins·Yawar Siddiqui·Julian Straub·Richard Newcombe·Vasileios Balntas·Jakob Engel

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Abstract

We present a novel human-in-the-loop approach to estimate 3D scene layout that uses human feedback from an egocentric standpoint. We study this approach through introduction of a novel local correction task, where users identify local errors and prompt a model to automatically correct them. Building on SceneScript, a state-of-the-art framework for 3D scene layout estimation that leverages structured language, we propose a solution that structures this problem as "infilling", a task studied in natural language processing. We train a multi-task version of SceneScript that maintains performance on global predictions while significantly improving its local correction ability. We integrate this into a human-in-the-loop system, enabling a user to iteratively refine scene layout estimates via a low-friction "one-click fix'' workflow. Our system enables the final refined layout to diverge from the training distribution, allowing for more accurate modelling of complex layouts.

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BibTeX RIS

Christopher Xie, Armen Avetisyan, Henry Howard-Jenkins, Yawar Siddiqui, Julian Straub, Richard Newcombe, Vasileios Balntas, Jakob Engel. 2025-03-14. Human-in-the-Loop Local Corrections of 3D Scene Layouts via Infilling. https://arxiv.org/abs/2503.11806

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