Papers › Language-driven Scene Synthesis using Multi-conditional Diffusion Model

Language-driven Scene Synthesis using Multi-conditional Diffusion Model

24 Oct 2023NeurIPS 2023 11arXiv:2310.15948archive 2025-07-28

An Vuong, Minh Nhat Vu, Toan Tien Nguyen, Baoru Huang, Dzung Nguyen, Thieu Vo, Anh Nguyen

Scene synthesis is a challenging problem with several industrial applications. Recently, substantial efforts have been directed to synthesize the scene using human motions, room layouts, or spatial graphs as the input. However, few studies have addressed this problem from multiple modalities, especially combining text prompts. In this paper, we propose a language-driven scene synthesis task, which is a new task that integrates text prompts, human motion, and existing objects for scene synthesis. Unlike other single-condition synthesis tasks, our problem involves multiple conditions and requires a strategy for processing and encoding them into a unified space. To address the challenge, we present a multi-conditional diffusion model, which differs from the implicit unification approach of other diffusion literature by explicitly predicting the guiding points for the original data distribution. We demonstrate that our approach is theoretically supportive. The intensive experiment results illustrate that our method outperforms state-of-the-art benchmarks and enables natural scene editing applications. The source code and dataset can be accessed at https://lang-scene-synth.github.io/.

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approx_standard_normal_cdf andvg3/LSDM/diffusion/losses.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · cfd76fd0d89574a4 · report
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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Indoor Scene Synthesis PRO-teXt LSDM CD 0.5365 #1 of 4 Archive leaderboard report
Indoor Scene Synthesis PRO-teXt LSDM EMD 0.5906 #1 of 4 Archive leaderboard report
Indoor Scene Synthesis PRO-teXt LSDM F1 0.5160 #1 of 4 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Diffusion

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