AHA！利用高斯点散布在多样场景中动画化人类
AHA! Animating Human Avatars in Diverse Scenes with Gaussian Splatting
AI总结：
本研究提出利用3D高斯点散布技术实现人类在多样场景中的动画化，通过解耦渲染与运动合成，提升单目视频中的人类动画效果。
摘要：
我们提出了一种新的框架，利用3D高斯点散布（3DGS）来在3D场景中动画化人类，3DGS是一种最近在新颖视角合成中取得最先进的逼真结果的神经场景表示方法，但尚未被广泛用于人类-场景动画和交互。与现有动画管道使用网格或点云作为底层3D表示不同，我们的方法引入了将3DGS作为动画化人类在场景中的3D表示。通过将人类和场景表示为高斯分布，我们的方法允许在人类与3D场景互动时保持几何一致的自由视角渲染。我们的关键见解是渲染可以与运动合成解耦，每个子问题可以独立解决，而无需配对的人-场景数据。我们方法的核心是高斯对齐的运动模块，该模块在不使用显式场景几何的情况下合成运动，使用基于不透明度的提示和投影高斯结构来指导人类位置和姿态对齐。为了确保自然的交互，我们进一步提出了一种人-场景高斯细化优化，以强制现实的接触和导航。我们评估了我们的方法在Scannet++和SuperSplat库中的场景，以及从稀疏和密集多视角人类捕捉中重建的avatara。最后，我们展示了我们的框架能够实现新颖的应用，例如编辑单目RGB视频的几何一致的自由视角渲染，展示3DGS在单目视频基础上的人类动画的独特优势。为了评估我们结果的完整质量，我们鼓励读者查看补充材料，网址为https://miraymen.github.io/aha/ 。
英文摘要：
We present a novel framework for animating humans in 3D scenes using 3D Gaussian Splatting (3DGS), a neural scene representation that has recently achieved state-of-the-art photorealistic results for novel-view synthesis but remains under-explored for human-scene animation and interaction. Unlike existing animation pipelines that use meshes or point clouds as the underlying 3D representation, our approach introduces the use of 3DGS as the 3D representation for animating humans in scenes. By representing humans and scenes as Gaussians, our approach allows geometry-consistent free-viewpoint rendering of humans interacting with 3D scenes. Our key insight is that rendering can be decoupled from motion synthesis, and each sub-problem can be addressed independently without the need for paired human-scene data. Central to our method is a Gaussian-aligned motion module that synthesizes motion without explicit scene geometry, using opacity-based cues and projected Gaussian structures to guide human placement and pose alignment. To ensure natural interactions, we further propose a human-scene Gaussian refinement optimization that enforces realistic contact and navigation. We evaluate our approach on scenes from Scannet++ and the SuperSplat library, and on avatars reconstructed from sparse and dense multi-view human capture. Finally, we demonstrate that our framework enables novel applications such as geometry-consistent free-viewpoint rendering of edited monocular RGB videos with newly animated humans, showcasing the unique advantages of 3DGS for monocular video-based human animation. To assess the full quality of our results, we encourage readers to view the supplementary material available at https://miraymen.github.io/aha/ .
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。
