  Several works have developed end-to-end pipelines for generating lip-synced
talking faces with various real-world applications, such as teaching and
language translation in videos. However, these prior works fail to create
realistic-looking videos since they focus little on people's expressions and
emotions. Moreover, these methods' effectiveness largely depends on the faces
in the training dataset, which means they may not perform well on unseen faces.
To mitigate this, we build a talking face generation framework conditioned on a
categorical emotion to generate videos with appropriate expressions, making
them more realistic and convincing. With a broad range of six emotions, i.e.,
\emph{happiness}, \emph{sadness}, \emph{fear}, \emph{anger}, \emph{disgust},
and \emph{neutral}, we show that our model can adapt to arbitrary identities,
emotions, and languages. Our proposed framework is equipped with a
user-friendly web interface with a real-time experience for talking face
generation with emotions. We also conduct a user study for subjective
evaluation of our interface's usability, design, and functionality. Project
page: https://midas.iiitd.edu.in/emo/
