Mixed reality applications require tracking the user's full-body motion to enable an immersive experience. However, typical head-mounted devices can only track head and hand movements, leading to a limited reconstruction of full-body motion due to variability in lower body configurations. We propose BoDiffusion -- a generative diffusion model for motion synthesis to tackle this under-constrained reconstruction problem. We present a time and space conditioning scheme that allows BoDiffusion to leverage sparse tracking inputs while generating smooth and realistic full-body motion sequences. To the best of our knowledge, this is the first approach that uses the reverse diffusion process to model full-body tracking as a conditional sequence generation task. We conduct experiments on the large-scale motion-capture dataset AMASS and show that our approach outperforms the state-of-the-art approaches by a significant margin in terms of full-body motion realism and joint reconstruction error.

翻译：混合现实应用需要追踪用户的全身运动从而实现沉浸式体验。然而，典型的头戴设备只能追踪头部和手部运动，导致由于下半身构型的变异性，全身运动的重建受到限制。我们提出BoDiffusion——一种用于运动合成的生成扩散模型，以应对这一欠约束重建问题。我们提出了一种时间和空间条件约束方案，使BoDiffusion能够在利用稀疏追踪输入的同时生成平滑且逼真的全身运动序列。据我们所知，这是首次利用逆向扩散过程将全身追踪建模为条件序列生成任务的方法。我们在大规模运动捕捉数据集AMASS上进行了实验，结果表明，我们的方法在全身运动逼真度和关节重建误差方面显著优于现有最先进方法。