Text to video generation via knowledge distillation

Text-to-video generation (T2V) has recently attracted more attention due to the wide application scenarios of video media. However, compared with the substantial advances in text-to-image generation (T2I), the research on T2V remains in its early stage. The difficulty mainly lies in maintaining the text-visual semantic consistency and the video temporal coherence. In this paper, we propose a novel distillation and translation GAN (DTGAN) to address these problems. First, we leverage knowledge distillation to guarantee semantic consistency. We distill text-visual mappings from a well-performing T2I teacher model and transfer it to our DTGAN. This knowledge serves as shared abstract features and high-level constraints for each frame in the generated videos. Second, we propose a novel visual recurrent unit (VRU) to achieve video temporal coherence. The VRU can generate frame sequences as well as process the temporal information across frames. It enables our generator to act as a multi-modal variant of the language model in neural machine translation task, which iteratively predicts the next frame based on the input text and the previously generated frames. We conduct experiments on two synthetic datasets (SBMG and TBMG) and one real-world dataset (MSVD). Qualitative and quantitative comparisons with state-of-the-art methods demonstrate that our DTGAN can generate results with better text-visual semantic consistency and temporal coherence.