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.

翻译：文本引导的扩散模型已彻底改变了图像与视频生成领域，并成功用于基于优化的三维物体合成。本文聚焦于尚未充分探索的文本到4D生成任务，通过引入额外时间维度的分数蒸馏方法，合成动态、可动画化的三维物体。与先前工作不同，我们采用一种新型组合式生成方法，将文本到图像、文本到视频及三维感知的多视图扩散模型相结合，在4D物体优化过程中提供反馈，从而同时实现时序一致性、高质量视觉外观与逼真几何结构。所提方法名为"对齐你的高斯"（AYG），采用带有变形场的动态三维高斯泼溅作为4D表示。AYG的关键创新在于一种新型正则化方法，通过约束运动三维高斯的分布来稳定优化过程并诱发运动。我们还提出运动放大机制与自回归合成方案，用于生成并组合多个4D序列以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，在定性与定量上超越先前工作，达到文本到4D生成的最优性能。得益于高斯4D表示，不同4D动画可无缝组合，我们对此进行了验证。AYG为动画制作、仿真模拟、数字内容创作及合成数据生成开辟了广阔前景。