SceneNAT: Masked Generative Modeling for Language-Guided Indoor Scene Synthesis

• URL: http://arxiv.org/abs/2601.07218v1
• Date: Mon, 12 Jan 2026 05:24:27 GMT
• Title: SceneNAT: Masked Generative Modeling for Language-Guided Indoor Scene Synthesis
• Authors: Jeongjun Choi, Yeonsoo Park, H. Jin Kim,
• Abstract summary: SceneNAT is a single-stage masked non-autoregressive Transformer that synthesizes complete 3D indoor scenes from natural language instructions.<n> SceneNAT is trained via masked modeling over fully discretized representations of both semantic and spatial attributes.<n>Experiments on the 3D-FRONT dataset demonstrate that SceneNAT achieves superior performance compared to state-of-the-art autoregressive and diffusion baselines.
• Score: 20.364473705766326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present SceneNAT, a single-stage masked non-autoregressive Transformer that synthesizes complete 3D indoor scenes from natural language instructions through only a few parallel decoding passes, offering improved performance and efficiency compared to prior state-of-the-art approaches. SceneNAT is trained via masked modeling over fully discretized representations of both semantic and spatial attributes. By applying a masking strategy at both the attribute level and the instance level, the model can better capture intra-object and inter-object structure. To boost relational reasoning, SceneNAT employs a dedicated triplet predictor for modeling the scene's layout and object relationships by mapping a set of learnable relation queries to a sparse set of symbolic triplets (subject, predicate, object). Extensive experiments on the 3D-FRONT dataset demonstrate that SceneNAT achieves superior performance compared to state-of-the-art autoregressive and diffusion baselines in both semantic compliance and spatial arrangement accuracy, while operating with substantially lower computational cost.

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