SceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene Generation

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Peking University

Jiaxiang Tang

NVIDIA

Ruijie Lu

Peking University

Gang Zeng

Peking University

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References (46)

[1]

SAGE: Scalable Agentic 3D Scene Generation for Embodied AI

2026Hongchi Xia, Xuan Li et al.

[2]

Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer

2025Z-Image Team, Huanqia Cai et al.

[3]

Text-to-Scene with Large Reasoning Models

2025Frédéric Berdoz, Luca A. Lanzendörfer et al.

[4]

SceneWeaver: All-in-One 3D Scene Synthesis with an Extensible and Self-Reflective Agent

2025Yandan Yang, Baoxiong Jia et al.

[5]

HOLODECK 2.0: Vision-Language-Guided 3D World Generation with Editing

2025Zixuan Bian, Ruohan Ren et al.

[6]

Towards Geometric and Textural Consistency 3D Scene Generation via Single Image-guided Model Generation and Layout Optimization

2025Xiang Tang, Ruotong Li et al.

[7]

ArtiScene: Language-Driven Artistic 3D Scene Generation Through Image Intermediary

2025Zeqi Gu, Yin Cui et al.

[8]

Agentic 3D Scene Generation with Spatially Contextualized VLMs

2025Xinhang Liu, Yu-Wing Tai et al.

[9]

3D Scene Generation: A Survey

2025Beichen Wen, Haozhe Xie et al.

[10]

Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation

2025Lu Ling, Chen-Hsuan Lin et al.

[11]

Scaling and Beyond: Advancing Spatial Reasoning in MLLMs Requires New Recipes

2025Huanyu Zhang, Chengzu Li et al.

[12]

SceneX: Procedural Controllable Large-Scale Scene Generation

2025Mengqi Zhou, Yuxi Wang et al.

[13]

Generative AI for Film Creation: A Survey of Recent Advances

2025Ruihang Zhang, Borou Yu et al.

[14]

Global-Local Tree Search in VLMs for 3D Indoor Scene Generation

2025Weiwei Deng, Mengshi Qi et al.

[15]

WorldCraft: Photo-Realistic 3D World Creation and Customization via LLM Agents

2025Xinhang Liu, Chi-Keung Tang et al.

[16]

Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

2025Zibo Zhao, Zeqiang Lai et al.

[17]

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

2025Adam Suma, Sam Dauncey

[18]

LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models

2024Fan-Yun Sun, Weiyu Liu et al.

[19]

The Scene Language: Representing Scenes with Programs, Words, and Embeddings

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[20]

Visual Prompting in Multimodal Large Language Models: A Survey

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Showing 20 of 46 references

Founder's Pitch

"SceneAssistant transforms text commands into high-quality 3D scenes with minimal user input."

3D Content Creation•Score: 8•View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

3/4 signals

7.5

Quick Build

4/4 signals

Series A Potential

4/4 signals

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Analysis model: GPT-4o · Last scored: 3/12/2026

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Why It Matters

This research streamlines the creation of 3D scenes from text, reducing the manual effort needed in industries like gaming and virtual reality.

Product Angle

To productize, integrate SceneAssistant into a cloud service platform for digital artists and developers that offers plug-and-play 3D scene generation via API.

Disruption

It replaces current labor-intensive methods of 3D content creation that require expertise in complex software.

Product Opportunity

There's a significant market in gaming, animation, and virtual reality sectors where there's demand for rapid, high-quality scene creation. Studios and content creators would pay for tools that reduce development time and cost.

Use Case Idea

A commercial application could be an online platform where users describe scenes in natural language, and the platform generates 3D models for games or VR environments instantly.

Science

The paper introduces an agentic framework using Vision-Language Models (VLMs) for open-vocabulary 3D scene generation. By leveraging visual feedback and a suite of action APIs, the system iteratively refines 3D scenes based on natural language descriptions.

Method & Eval

Tested through a combination of qualitative human evaluation and quantitative benchmarks showing superior performance over existing methods in terms of spatial accuracy and scene coherence.

Caveats

The approach depends on the inherent capabilities of VLMs, which might not fully capture or interpret user intent in complex scenarios.

Author Intelligence

Jun Luo

LEAD

Peking University

Jiaxiang Tang

NVIDIA

Ruijie Lu

Peking University

Gang Zeng

Peking University

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