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Under review as a conference paper at ICLR 2027

From Plausible Scenes to Executable Worlds: Script-based 3D Indoor Scene Generation

Abstract

Script-based 3D indoor scene generation aims to construct 3D environments that faithfully realize the behaviors described in textual narratives. Existing scene generation methods, however, primarily focus on visual plausibility and semantic consistency, leaving an important question largely unexplored: can the generated scene actually support the prescribed behaviors? We argue that a behaviorally meaningful scene should constitute an executable world, where scripted actions are feasible not only individually but also as a coherent sequence. Based on this perspective, we identify three levels of executable requirements: spatial executability, which requires characters to reach the locations needed for consecutive actions; functional executability, which requires scene objects to geometrically and functionally support their intended interactions; and sequential executability, which requires successive actions to remain coherently executable over time. These requirements motivate us to move beyond one-shot scene generation toward an explicit generation-diagnosis-repair paradigm. To study it, we first construct FilmScene, a character behavior-centric dataset containing script-3D scene pairs, with scene-grounded interaction objects and action chains, together with real-world out-of-distribution test sets. Building upon this benchmark, we propose Script-to-Scene (S2S), a closed-loop framework that first generates a plausible scene, explicitly diagnoses violations of executable constraints, and then performs targeted scene repair while preserving valid scene structure. By combining spatial reachability analysis, functional compatibility assessment, behavioral sequence verification, S2S progressively transforms plausible scenes into executable worlds via iterative local scene editing. Extensive experiments on FilmScene and OOD test sets demonstrate that S2S improves both overall scene quality and the ability of generated environments to support script-driven character behaviors.

open until 14 Dec 2026

est. 32% chance this paper gets accepted at ICLR 2027.

Reject 68%Accept 32%

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