Sign language is a language that uses a combination of hand gestures and lip movements for people with hearing impairment to communicate. In Indonesia there are two sign language systems used, Sign System for Indonesian Language known as SIBI (Sistem Isyarat Bahasa Indonesia) recognizes as the official sign language system by the Indonesian Government. This research is focused on the generation process of skeleton sequence; in which represent a SIBI hand gesture excluding the finger joints. The hand skeleton that will be generated from the generation process is limited to root-word gestures only. Some researchers were using a Restricted Boltzmann Machine model and its variant known as Deep Belief Networks (DBN) to solve the sequence modelling problems. One of DBN variants is Sigmoid Belief Network (SBN). An SBN is a Bayesian network that models a binary visible vector. Deep Temporal Sigmoid Belief Network (DTSBN) is a sequence of SBNs (with deep architecture) arranged in such way that at any given time step has a fully generative process capability, where data are readily generated from the model using ancestral sampling. Since, DTSBN performance is quite novel for this particular case, we decided to implement the DTSBN model using the SIBI dataset from the previous research to construct generated hand-skeleton gestures which represent SIBI’s root-word gestures. Based on the success of the experimental DTSBN model that has been successfully generated new skeleton sequences, which represent a SIBI hand gesture. Some of the inputs to the model include cartesian coordinates from shoulder joints, elbow joints, and wrist joints and the newly generated data are proven have no significant difference with the actual data set.