论文标题
标志：基于流量的3D化身从稀疏观测中产生
FLAG: Flow-based 3D Avatar Generation from Sparse Observations
论文作者
论文摘要
为了代表混合现实应用程序进行协作和沟通，我们需要产生现实和忠实的头像构成。但是，可以从头部安装的设备（HMD）应用于此任务的信号流通常仅限于头部姿势和手动姿势估计。尽管这些信号很有价值，但它们是人体的不完整代表，这使得产生忠实的全身化身具有挑战性。我们通过从稀疏观察中开发基于流动的3D人体的基于流动的生成模型来应对这一挑战，其中我们不仅可以学习3D人类姿势的条件分布，而且还学到了从观测到潜在空间的概率映射，我们可以从中产生一个可产生合理的姿势以及关节的不确定性估计。我们表明，我们的方法不仅是一个强大的预测模型，而且还可以在不同的优化设置中充当有效的姿势，在不同的优化设置中，良好的初始潜在代码起着主要作用。
To represent people in mixed reality applications for collaboration and communication, we need to generate realistic and faithful avatar poses. However, the signal streams that can be applied for this task from head-mounted devices (HMDs) are typically limited to head pose and hand pose estimates. While these signals are valuable, they are an incomplete representation of the human body, making it challenging to generate a faithful full-body avatar. We address this challenge by developing a flow-based generative model of the 3D human body from sparse observations, wherein we learn not only a conditional distribution of 3D human pose, but also a probabilistic mapping from observations to the latent space from which we can generate a plausible pose along with uncertainty estimates for the joints. We show that our approach is not only a strong predictive model, but can also act as an efficient pose prior in different optimization settings where a good initial latent code plays a major role.
