Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models
Align Your Gaussians (AYG) combines dynamic 3D Gaussian splatting with deformation fields and multi-modal diffusion feedback to achieve state-of-the-art text-to-4D scene synthesis, outperforming prior methods.
Key Findings
Methodology
AYG integrates dynamic 3D Gaussian splatting with deformation networks, leveraging multi-modal diffusion models such as Stable Diffusion, MVDream, and VideoLDM. The process begins with generating a high-quality static 3D shape via multi-view diffusion, then optimizes scene dynamics through a learned deformation field guided by text-to-video diffusion feedback. Regularization via Jensen-Shannon divergence stabilizes Gaussian distributions, while motion amplification enhances dynamic effects. An autoregressive scheme extends sequences, and all components are trained within a score distillation framework that fuses multi-modal gradients for coherent, vivid 4D scene synthesis.
Key Results
- On datasets like Objaverse and WebVid-10M, AYG surpasses MAV3D with a 53.6% user preference rate, producing diverse, high-fidelity 4D scenes with superior structural consistency and visual quality. Quantitative metrics such as scene realism and temporal coherence show significant improvements. Ablation studies confirm the importance of regularization, motion amplification, and autoregressive extension, with performance drops exceeding 20% when any component is removed.
- The model demonstrates strong capability in long sequence generation and scene composition, enabling seamless merging of multiple dynamic scenes. The regularization ensures scene stability over time, while the multi-modal feedback refines both geometry and motion, leading to more realistic and engaging animations.
- Experimental results highlight that the combination of Gaussian regularization, multi-modal feedback, and autoregressive sequence extension is crucial for achieving state-of-the-art performance, with ablations showing performance degradation without each component.
Significance
This work advances the frontier of dynamic 3D scene synthesis by enabling long, complex, and realistic 4D animations guided solely by text prompts. It addresses key challenges in maintaining geometric consistency, temporal coherence, and scene composability, which are vital for applications in virtual reality, gaming, and cinematic content creation. The high efficiency of Gaussian splatting combined with multi-modal diffusion feedback opens new avenues for large-scale scene generation and synthetic data production, pushing the boundaries of what is possible in AI-driven virtual environment creation.
Technical Contribution
The paper introduces a novel 4D scene representation based on dynamic Gaussian splatting coupled with deformation networks, enabling flexible modeling of complex scene dynamics. It innovatively combines multiple diffusion models within a score distillation framework, leveraging multi-modal feedback to optimize scene geometry and motion simultaneously. The regularization via Jensen-Shannon divergence ensures stability, while the motion amplification and autoregressive sequence extension significantly improve long-term dynamic synthesis. These contributions collectively enable high-quality, scalable, and composable 4D scene generation, setting new benchmarks in the field.
Novelty
This is the first work to utilize dynamic 3D Gaussian splatting as a core 4D scene representation for text-guided synthesis. Unlike prior methods such as MAV3D, which rely on NeRF-based models and limited sequence lengths, AYG explicitly models scene dynamics through deformation fields and supports seamless scene composition. The integration of multi-modal diffusion feedback within a score distillation framework, combined with regularization and autoregressive extensions, represents a significant innovation that pushes the state-of-the-art in 4D content creation.
Limitations
- Despite its strengths, the model struggles with extremely complex scenes or very long sequences, where motion blur and geometric drift can occur due to limited deformation expressivity. Computational costs remain high, limiting real-time applications. The Gaussian parameters require careful tuning, and performance may vary across different scene types, indicating a need for more robust parameterization and optimization strategies.
Future Work
Future research will focus on enhancing deformation network capacity for more complex dynamics, reducing computational overhead, and improving stability for ultra-long sequences. Incorporating reinforcement learning or self-supervised signals could further improve scene diversity and realism. Extending the framework to real-time interactive applications and higher resolution outputs will broaden practical deployment, especially in immersive virtual environments and interactive media.
AI Executive Summary
The rapid growth of virtual content demands innovative methods for dynamic scene creation. Traditional approaches, often limited to static models or short animations, fall short in producing long, coherent, and richly detailed 4D environments. Addressing this gap, the present work introduces Align Your Gaussians (AYG), a novel framework that synthesizes dynamic 4D scenes guided solely by textual prompts.
AYG’s core innovation lies in representing scenes via dynamic 3D Gaussian splatting combined with deformation fields, enabling flexible modeling of complex scene motions. The process begins with generating a high-quality static shape through multi-view diffusion models like MVDream, which is then refined into a dynamic scene by optimizing a deformation network. This optimization is guided by multi-modal feedback from text-to-image and text-to-video diffusion models, ensuring both visual fidelity and temporal coherence.
A key technical advance is the regularization of Gaussian distributions using Jensen-Shannon divergence, which stabilizes scene evolution and prevents unrealistic distortions. To generate longer sequences, the authors introduce an autoregressive extension scheme, interpolating deformation fields between overlapping segments, thus creating seamless, extended animations. Additionally, motion amplification techniques are employed to enhance dynamic effects, making animations more vivid.
Experimental results demonstrate that AYG outperforms prior state-of-the-art methods like MAV3D, achieving higher user preference scores and more realistic, diverse scenes. Quantitative metrics confirm improvements in structural consistency, visual quality, and scene complexity. The ability to compose multiple 4D scenes seamlessly highlights the flexibility of the Gaussian-based representation.
This work significantly advances the field of AI-driven scene synthesis, opening new possibilities for virtual reality, gaming, and cinematic content creation. Its scalable, modular approach paves the way for real-time applications and large-scale scene generation, although challenges remain in computational efficiency and handling extremely complex scenarios. Overall, AYG sets a new benchmark for text-guided dynamic scene synthesis, inspiring future research in this exciting domain.
Deep Analysis
Background
近年来，三维场景生成技术经历了从传统网格、点云到神经辐射场（NeRF）的快速发展。早期方法如Voxel Grid和点云表示在存储和渲染效率上存在瓶颈。NeRF引入连续体积渲染极大提升了细节表现，但计算成本较高。Gaussian Splatting作为一种高效的点云表示，支持大规模场景的快速渲染。扩散模型在图像和视频生成中表现出色，逐步被引入3D内容合成，推动静态3D模型的高质量生成。然而，动态场景的连续性和复杂性仍是难点，尤其在保持时间一致性和几何合理性方面。
Core Problem
现有方法多集中于静态场景或短时动画，难以实现长序列、复杂动态的高质量合成。NeRF等模型在长时间序列中易出现几何漂移和运动模糊，限制了虚拟场景的真实性和连续性。如何在保证几何一致性的同时，生成丰富、真实的动态场景，成为核心难题。多模态反馈融合、长序列优化和场景的可组合性，都是亟待攻克的问题。
Innovation
本研究的创新点包括：1）提出基于动态高斯点云和变形场的4D场景表示，支持复杂动态和场景拼接；2）结合多模态扩散模型（文本到图像、视频、多视角）进行联合优化，提升场景质量与时间一致性；3）引入JSD正则化确保点云稳定，增强动态表现；4）设计自回归方案延长场景序列，实现长时动画；5）融合score distillation与多模态反馈，达到SOTA性能。这些创新共同推动了4D内容生成的边界。
Methodology
- �� 利用多视角扩散模型生成静态3D形状作为基础。
- �� 引入变形场MLP，预测场景在不同时间点的变形。
- �� 结合文本到视频和多视角模型的梯度，优化场景几何与动态。
- �� 采用JSD正则化，确保高斯点云的稳定性，避免运动模糊。
- �� 运动放大机制提升动态丰富性。
- �� 通过自回归方案，将多个场景连续拼接，生成长序列。
- �� 不同4D场景通过高斯点云的可组合性实现大场景拼接。
Experiments
在Objaverse和WebVid-10M数据集上，模型通过结构一致性、视觉质量等指标优于MAV3D，用户偏好达53.6%。消融实验验证正则化、运动放大和自回归方案的关键作用。长序列和场景拼接显示模型在复杂动态和场景组合方面表现优越。参数调优包括高斯尺度、变形网络深度和多模态模型权重，确保多场景、多时间尺度的稳定性。
Results
模型在多场景、多时间点下实现高质量4D动画，定量指标优于竞品，偏好度明显提升。长序列和场景拼接效果良好，验证了高斯点云的可组合性。消融研究确认正则化和运动放大是性能提升的关键。多模态反馈融合显著改善场景细节和动态表现，展示了模型的强大适应性。
Applications
广泛应用于虚拟现实、影视动画、游戏开发、虚拟仿真等领域。模型能自动生成复杂动态场景，减少人工成本，支持大规模场景拼接与定制。未来结合实时交互技术，推动虚拟内容的个性化和沉浸式体验。
Limitations & Outlook
在极端复杂或超长序列中，运动模糊和几何漂移仍可能发生。训练成本高，依赖大量多模态模型计算资源。高斯参数调优敏感，不同场景表现不一。未来需提升鲁棒性和效率，支持实时应用。
Plain Language Accessible to non-experts
想象你在做一个动画电影的场景设计。以前，设计师只能用静态图片或短动画片段，但现在他们希望能一键生成一个长长的、充满动作的虚拟世界。这个过程就像用一堆特殊的“点”——高斯点云——来代表场景中的每个物体，每个点都可以变形、移动，像气体一样灵活。通过给电脑一些文字描述，比如“一个在森林中奔跑的动物”，电脑会用一种叫扩散模型的“智能画笔”反复试错，把场景变得越来越真实。这些点云会随着时间变化，模拟出动物奔跑、树叶飘落的动态效果。研究团队还设计了让这些点云保持稳定的“规矩”，让动画看起来更自然。最终，他们能生成连续不断、细节丰富、可以拼接的大场景，就像用魔法打造的虚拟世界一样。
ELI14 Explained like you're 14
想象你在玩一个超级酷的游戏，你可以让虚拟世界里的东西动起来，比如让一只猫跑、树叶飘落。以前，制作这些动画很难，要花很多时间和技术。现在，有一种新方法可以让电脑自己用文字描述，自动生成这些动态场景。它就像用很多小气球（叫高斯点云）组成一个虚拟的场景，这些气球可以变形、移动，模拟出真实的动作。研究人员教电脑用一种叫扩散模型的“智能画笔”反复试错，把场景变得越来越真实。它还能让不同的场景拼接在一起，像拼积木一样，组成更大的虚拟世界。这就像你用乐高拼搭一个完整的城市，不仅快，还很逼真。未来，这项技术可以用在电影、游戏、虚拟现实里，让虚拟世界变得更丰富、更真实，甚至可以自己动起来！
Abstract
Text-guided diffusion models have revolutionized image and video generation and have also been successfully used for optimization-based 3D object synthesis. Here, we instead focus on the underexplored text-to-4D setting and synthesize dynamic, animated 3D objects using score distillation methods with an additional temporal dimension. Compared to previous work, we pursue a novel compositional generation-based approach, and combine text-to-image, text-to-video, and 3D-aware multiview diffusion models to provide feedback during 4D object optimization, thereby simultaneously enforcing temporal consistency, high-quality visual appearance and realistic geometry. Our method, called Align Your Gaussians (AYG), leverages dynamic 3D Gaussian Splatting with deformation fields as 4D representation. Crucial to AYG is a novel method to regularize the distribution of the moving 3D Gaussians and thereby stabilize the optimization and induce motion. We also propose a motion amplification mechanism as well as a new autoregressive synthesis scheme to generate and combine multiple 4D sequences for longer generation. These techniques allow us to synthesize vivid dynamic scenes, outperform previous work qualitatively and quantitatively and achieve state-of-the-art text-to-4D performance. Due to the Gaussian 4D representation, different 4D animations can be seamlessly combined, as we demonstrate. AYG opens up promising avenues for animation, simulation and digital content creation as well as synthetic data generation.