# Comp4D: LLM-Guided Compositional 4D Scene Generation

**Authors:** Dejia Xu, Hanwen Liang, Neel P. Bhatt, Hezhen Hu, Hanxue Liang, Konstantinos N. Plataniotis, Zhangyang Wang  
**arXiv:** [2403.16993](https://arxiv.org/abs/2403.16993)  
**Date:** 2024-03-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2403.16993v1/

## Key points

- Significant interest in developing diffusion models for 2D and 3D content creation
- Desire to generate 4D content is growing
- Current methodologies limited to object-centric generation due to lack of comprehensive 3D scene datasets
- Introduction of Comp4D framework by researchers Dejia Xu, Hanwen Liang, Neel P. Bhatt, Hezhen Hu, Hanxue Liang, Konstantinos N. Plataniotis, and Zhangyang Wang
- Framework constructs each 4D object within the scene individually
- Utilizes Large Language Models (LLMs) to break down input text prompt into distinct entities and map out trajectories
- Positions objects along designated paths to create a compositional 4D scene
- Technique incorporates pre-trained diffusion models spanning text-to-image, text-to-video, and text-to-3D domains for enhanced quality
- Demonstrated exceptional capabilities in creating 4D content with superior visual quality, motion fidelity, and object interactions compared to existing methods
- Ability to construct dynamic scenes with multiple moving components opens up new possibilities for immersive content creation

## Summary

In recent years, there has been a significant interest in the development of diffusion models for 2D and 3D content creation. This has led to a growing desire to generate 4D content. However, the lack of comprehensive 3D scene datasets has limited current methodologies to focus primarily on object-centric generation. To address this challenge, a team of researchers including Dejia Xu, Hanwen Liang, Neel P. Bhatt, Hezhen Hu, Hanxue Liang, Konstantinos N. Plataniotis, and Zhangyang Wang have introduced Comp4D: a groundbreaking framework for Compositional 4D Generation. takes a unique approach by individually constructing each 4D object within the scene. Leveraging Large Language Models (LLMs), the framework initially breaks down an input text prompt into distinct entities and then maps out their trajectories. Subsequently, it meticulously positions these objects along their designated paths to create a compositional 4D scene. To further enhance the quality of the generated scenes, is utilized guided by predefined trajectories. This technique incorporates pre-trained diffusion models spanning text-to-image, text-to-video, and text-to-3D domains. Through extensive experimentation and evaluation, has demonstrated exceptional capabilities in creating 4D content when compared to existing methods. The results showcase superior visual quality with enhanced motion fidelity and object interactions. The framework's ability to construct dynamic scenes with multiple moving components opens up new possibilities for immersive content creation across various applications. The authors' work represents a significant advancement in the field of generative modeling and lays the foundation for future developments in .