  Object recognition, commonly performed by a camera, is a fundamental
requirement for robots to complete complex tasks. Some tasks require
recognizing objects far from the robot's camera. A challenging example is
Ultra-Range Gesture Recognition (URGR) in human-robot interaction where the
user exhibits directive gestures at a distance of up to 25~m from the robot.
However, training a model to recognize hardly visible objects located in
ultra-range requires an exhaustive collection of a significant amount of
labeled samples. The generation of synthetic training datasets is a recent
solution to the lack of real-world data, while unable to properly replicate the
realistic visual characteristics of distant objects in images. In this letter,
we propose the Diffusion in Ultra-Range (DUR) framework based on a Diffusion
model to generate labeled images of distant objects in various scenes. The DUR
generator receives a desired distance and class (e.g., gesture) and outputs a
corresponding synthetic image. We apply DUR to train a URGR model with
directive gestures in which fine details of the gesturing hand are challenging
to distinguish. DUR is compared to other types of generative models showcasing
superiority both in fidelity and in recognition success rate when training a
URGR model. More importantly, training a DUR model on a limited amount of real
data and then using it to generate synthetic data for training a URGR model
outperforms directly training the URGR model on real data. The synthetic-based
URGR model is also demonstrated in gesture-based direction of a ground robot.
