Gaussians-to-Life：文本驱动的3D高斯泼溅场景动画
Gaussians-to-Life: Text-Driven Animation of 3D Gaussian Splatting Scenes
AI总结：
本文提出Gaussians2Life方法，结合视频扩散模型与2D到3D运动提升技术，实现对已有3D高斯泼溅场景中多种物体的文本驱动逼真动画，突破了以往方法仅适用于角色或单一物体的局限。
摘要：
最先进的新视角合成方法在静态3D场景的多视角捕捉中取得了令人印象深刻的结果。然而，重建的场景仍然缺乏“生动性”，而这是创造引人入胜的3D体验的关键要素。最近，新型视频扩散模型能够生成具有复杂运动的逼真视频，并实现2D图像的动画化，但它们无法直接用于动画化3D场景，因为缺乏多视角一致性。为了给静态世界注入生命，我们提出了Gaussians2Life，这是一种在高斯泼溅（Gaussian Splatting）表示中对高质量3D场景的局部进行动画化的方法。我们的核心思想是利用强大的视频扩散模型作为模型的生成组件，并将其与一种将2D视频提升为有意义的3D运动的鲁棒技术相结合。我们发现，与先前工作相比，该方法能够对复杂的、预先存在的3D场景进行逼真动画，并且能够对多种多样的物体类别进行动画化，而相关工作大多集中于基于先验的角色动画或单个3D物体。我们的模型能够为任意场景创建一致的、沉浸式的3D体验。
英文摘要：
State-of-the-art novel view synthesis methods achieve impressive results for multi-view captures of static 3D scenes. However, the reconstructed scenes still lack "liveliness," a key component for creating engaging 3D experiences. Recently, novel video diffusion models generate realistic videos with complex motion and enable animations of 2D images, however they cannot naively be used to animate 3D scenes as they lack multi-view consistency. To breathe life into the static world, we propose Gaussians2Life, a method for animating parts of high-quality 3D scenes in a Gaussian Splatting representation. Our key idea is to leverage powerful video diffusion models as the generative component of our model and to combine these with a robust technique to lift 2D videos into meaningful 3D motion. We find that, in contrast to prior work, this enables realistic animations of complex, pre-existing 3D scenes and further enables the animation of a large variety of object classes, while related work is mostly focused on prior-based character animation, or single 3D objects. Our model enables the creation of consistent, immersive 3D experiences for arbitrary scenes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
