13 DAILY CVPR Wednesday NRDF pose priors are versatile and can be applied to various downstream tasks such as pose denoising, 3D pose estimation from single images, and solving inverse kinematics from sparse observations. “Sometimes existing methods return 3D poses where the image overlay is good, but if you view it from another point of view in 3D, it’s implausible,” Yannan points out. “The 3D pose itself may have self-occlusions, interpenetrations, and also some implausible pose patterns, like a knee bending outwards.” Besides humans, NRDF can be extended to any articulated shapes, such as hand and animal poses. It can return plausible and valid results with only wrists and ankles as surface markers. Yannan outlines two critical ideas presented in NRDF. Firstly, a novel sampling approach to address inaccurate distance prediction, where there are no training samples near the manifold. “During the training data preparation, we aim to draw more samples near the surface, with a gradual decrease as we move to the faraway regions,” he explains. “In the distribution of PoseNDF, there is a huge gap between the zero-level-set and the mean, which lies in the center with a big distance value. After we propose the sampling algorithm, we could obtain a distribution shape like a halfGaussian distribution. Actually, it could be any distribution that the user specifies. It could be an exponential distribution or a uniform distribution.” Most distance fields work with 3D point clouds, so they are learning 3D shapes in Euclidean space, but this work features high-dimensional articulated poses represented by K quaternions in the product space of SO(3). “Sampling points in Euclidean space is totally different from directly sampling rotations,” Yannan tells us. “The special thing about our work is we propose an easy way to
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