Neural Machine Translation (NMT) has made remarkable progress with the Transformer architecture, but it remains challenged by ambiguous or context-dependent language that can benefit from visual context. Multimodal Machine Translation (MMT) addresses this by incorporating information from images into the translation process to improve accuracy, especially for image-related or ambiguous content. However, most MMT approaches rely on multimodal parallel corpora, which are scarce for many language pairs. This thesis introduces a CycleGAN-based multimodal translation architecture allowing training without direct sentence-pair annotation by utilizing images as a pivot from one language to another. The model synthesizes visual-semantic representations from CLIPTrans such that the source and target language representations are aligned in a common vision-language feature space.