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After 2D ray sampling process depicted in Sec. The total number of parameters of our network is 25M. In Section 3.4 of the main manuscript, we introduce the 3D intersection-aware hand feature This process enables the extraction of global information from the hand joints. Our training process involves the utilization of five distinct types of data samples. In this section, we provide the corresponding table (Tab.

Codes are available at https://github.com/ZhangCYG/DDFHO.

We make two surprising observations.

The chain structure is also convenient to handle streaming data as we will explain later. We first give a brief introduction to the EP and CEP framework. Step 2. We construct a tilted distribution to combine the true likelihood, Step 3. We project the tilted distribution back to the exponential family, q KL( null p nullq) where q belongs to the exponential family. Step 4. We update the approximation term by's in parallel, and uses damping to avoid divergence. The above computation are very conveniently to implement.

Recent works have revealed the role of geometric partitions and Sperner's

In addition, we demonstrate the scheme's satisfactory architecture generality by providing QuantSR-C