In the trainable unit of constraint module, a zero initialized MLP layer is attached to each trainable encoder block. Similar to ControlNet, the output of each zero MLP layer is added to the locked GT block of decoder in constraint module. This collecting mechanism is served as skip-connection, linking the output of the trainable encoder layer to the input of the decoder layer in the opposite order. Finally, the graph output of constraint module 𝐺 ) = {𝑉 ) , 𝐸 ) , 𝑅 ) } is combined with the output of 3D generative module 𝐺 & to form the final output of GrCN.