A well-known problem in video conferencing is gaze mismatch. Instead of relying exclusively on online captured data for rendering, a recent work first trains offline dictionaries using a large image database of movie and TV stars to learn "beautiful" features. During real-time conferencing, one can then simultaneously correct gaze and beautify the subject's facial components in single images by seeking sparse linear combination of pre-trained dictionary atoms for face reconstruction. Extending on this work, we focus on joint gaze correction / face beautification for video. First, we define a large search space invariant to scale, shift and rotation for facial feature beautification based on SIFT. We then address two practical issues unique to video: i) how beautified results can be temporally consistent across group of pictures (GOP), and ii) how blinking eyes can be beautified even though the training database contains only open-eye facial images. Experimental results show that our method achieves the desired temporal consistency, and the blinking process is smooth and natural.