With direct access to human-written reference as memory, retrieval-augmented generation has achieved much progress in a wide range of text generation tasks.

It imposes lower and upper bound constraints on the number of facilities opened for each label, ensuring fair representation of all demographic groups by the selected facilities.

In this work, we propose a novel 3D Transformer framework called LART for 3D motion transfer.

On one hand, the knowledge learned from public data is implicitly transferred into the private model.