  Image-to-image translation and voice conversion enable the generation of a
new facial image and voice while maintaining some of the semantics such as a
pose in an image and linguistic content in audio, respectively. They can aid in
the content-creation process in many applications. However, as they are limited
to the conversion within each modality, matching the impression of the
generated face and voice remains an open question. We propose a cross-modal
style transfer framework called XFaVoT that jointly learns four tasks: image
translation and voice conversion tasks with audio or image guidance, which
enables the generation of ``face that matches given voice" and ``voice that
matches given face", and intra-modality translation tasks with a single
framework. Experimental results on multiple datasets show that XFaVoT achieves
cross-modal style translation of image and voice, outperforming baselines in
terms of quality, diversity, and face-voice correspondence.
