  Non-verbal behavior is essential for embodied agents like social robots,
virtual avatars, and digital humans. Existing behavior authoring approaches
including keyframe animation and motion capture are too expensive to use when
there are numerous utterances requiring gestures. Automatic generation methods
show promising results, but their output quality is not satisfactory yet, and
it is hard to modify outputs as a gesture designer wants. We introduce a new
gesture generation toolkit, named SGToolkit, which gives a higher quality
output than automatic methods and is more efficient than manual authoring. For
the toolkit, we propose a neural generative model that synthesizes gestures
from speech and accommodates fine-level pose controls and coarse-level style
controls from users. The user study with 24 participants showed that the
toolkit is favorable over manual authoring, and the generated gestures were
also human-like and appropriate to input speech. The SGToolkit is platform
agnostic, and the code is available at https://github.com/ai4r/SGToolkit.
