化身长出双腿：利用扩散模型从稀疏追踪输入生成平滑人体运动
Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
AI总结：
本文提出AGRoL条件扩散模型，基于MLP架构和新颖条件化方案，从HMD稀疏上半身追踪信号实时生成准确平滑的全身运动，并在AMASS数据集上超越现有方法。
摘要：
随着AR/VR应用近期迅速普及，对3D全身化身进行逼真且精确的控制已成为一项高度需求的功能。一个特别的挑战是，独立HMD（头戴式设备）只能提供稀疏的追踪信号，通常仅限于追踪用户的头部和手腕。尽管该信号足以重建上半身运动，但下半身未被追踪，必须根据上半身关节提供的有限信息进行合成。在本文中，我们提出了AGRoL，这是一种新颖的条件扩散模型，专门设计用于在给定稀疏上半身追踪信号的情况下追踪全身。我们的模型基于简单的多层感知机（MLP）架构和一种新颖的运动数据条件化方案。它能够预测准确且平滑的全身运动，尤其是具有挑战性的下半身运动。与常见的扩散架构不同，我们的紧凑架构能够实时运行，使其适用于在线身体追踪应用。我们在AMASS运动捕捉数据集上训练和评估了我们的模型，并证明我们的方法在生成运动的准确性和平滑性方面优于最先进的方法。我们进一步通过大量实验和消融研究验证了我们的设计选择。
英文摘要：
With the recent surge in popularity of AR/VR applications, realistic and accurate control of 3D full-body avatars has become a highly demanded feature. A particular challenge is that only a sparse tracking signal is available from standalone HMDs (Head Mounted Devices), often limited to tracking the user's head and wrists. While this signal is resourceful for reconstructing the upper body motion, the lower body is not tracked and must be synthesized from the limited information provided by the upper body joints. In this paper, we present AGRoL, a novel conditional diffusion model specifically designed to track full bodies given sparse upper-body tracking signals. Our model is based on a simple multi-layer perceptron (MLP) architecture and a novel conditioning scheme for motion data. It can predict accurate and smooth full-body motion, particularly the challenging lower body movement. Unlike common diffusion architectures, our compact architecture can run in real-time, making it suitable for online body-tracking applications. We train and evaluate our model on AMASS motion capture dataset, and demonstrate that our approach outperforms state-of-the-art methods in generated motion accuracy and smoothness. We further justify our design choices through extensive experiments and ablation studies.
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。
