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.
本文中，我们重点关注尚未充分探索的文本到4D（三维动画）转换，并使用带有额外时间维度的分数蒸馏方法合成动态的、有动画效果的三维物体。与之前的工作相比，我们采取了一种全新的组合式生成方法，结合了文本到图像、文本到视频和具有三维意识的多视图扩散模型，以在4D物体优化过程中提供反馈，从而同时实现时间一致性、高质量视觉外观和真实的几何形状。我们的方法，称为“对齐你的高斯”(Align Your Gaussians, AYG)，利用动态三维高斯飞溅与变形场作为4D表示。AYG的关键是一种新颖的方法，用于规范移动的三维高斯的分布，从而稳定优化过程并诱导运动。我们还提出了一种运动放大机制以及一种新的自回归合成方案，用于生成和组合多个4D序列，实现更长时间的生成。这些技术使我们能够合成生动的动态场景，从定性和定量上超越以往的工作，并实现最先进的文本到4D性能。由于采用了高斯4D表示，不同的4D动画可以无缝结合，正如我们所展示的。AYG为动画、模拟、数字内容创作以及合成数据生成开辟了有前途的途径。