Video-guided machine translation (VMT) translates text by leveraging the context provided by the accompanying video. This additional contextual information helps improve translation quality by disambiguating ambiguous words or explicitly indicating attributes such as the colour or gender of objects and people. Prior works have extensively explored VMT; however, most approaches train models from scratch, which requires large amounts of data. Additionally, the majority of VMT research has focused on the English–Chinese language pair. In this paper, we explore training strategies for VMT using only a limited amount of data and extend the task to Indic languages, specifically Hindi, Marathi and Odia. We also propose a synthetic data framework that is effective for training VMT models in low-resource language settings. Our results demonstrate an improvement of +1.75 BLEU compared to baseline models.