# Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models

**Authors:** Huan Ling, Seung Wook Kim, Antonio Torralba, Sanja Fidler, Karsten Kreis  
**arXiv:** [2312.13763](https://arxiv.org/abs/2312.13763)  
**Date:** 2023-12-21  
**Summary page:** https://www.summarizepaper.com/en/paper/2312.13763v2/

## Key points

- Authors introduce a novel approach to text-guided diffusion models for image and video generation
- Focus on text-to-4D setting to synthesize dynamic, animated 3D objects
- Adoption of compositional generation-based strategy combining text-to-image, text-to-video, and 3D-aware multiview diffusion models
- Leveraging dynamic 3D Gaussian Splatting with deformation fields for representation of 4D objects
- Introduction of novel regularization technique to stabilize moving 3D Gaussians distribution for effective motion induction
- Motion amplification mechanism and autoregressive synthesis scheme for generating and combining multiple 4D sequences
- Ability to seamlessly combine different 4D animations due to Gaussian representation employed
- Significant advancement in text-guided diffusion models for dynamic 3D object synthesis in the text-to-4D domain

## Summary

In their paper titled "Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models," authors Huan Ling, Seung Wook Kim, Antonio Torralba, Sanja Fidler, and Karsten Kreis introduce a novel approach to text-guided diffusion models for image and video generation. The focus of their work lies in the exploration of the text-to-4D setting, where they aim to synthesize dynamic, animated 3D objects by incorporating score distillation methods with an additional temporal dimension. Unlike previous approaches, the authors adopt a compositional generation-based strategy that combines text-to-image, text-to-video, and 3D-aware multiview diffusion models. This unique combination allows for feedback during the optimization process of 4D object synthesis, ensuring temporal consistency, high-quality visual appearance, and realistic geometry simultaneously. Their proposed method , leverages dynamic 3D Gaussian Splatting with deformation fields as a representation for the 4D objects. A key contribution of  is a novel regularization technique that stabilizes the distribution of moving 3D Gaussians to induce motion effectively during optimization. Additionally, the authors introduce a motion amplification mechanism and an autoregressive synthesis scheme to generate and combine multiple 4D sequences for extended animation sequences. These innovative techniques enable the synthesis of vivid dynamic scenes that outperform existing methods both qualitatively and quantitatively. One notable advantage of  is its ability to seamlessly combine different 4D animations due to the Gaussian representation employed. The authors demonstrate how this capability opens up promising avenues for applications in animation, simulation, digital content creation, and synthetic data generation. Overall,  represents a significant advancement in text-guided diffusion models for dynamic 3D object synthesis in the text-to-4D domain.