Lack of audio-video synchronization is a common problem during television broadcasts and video conferencing, leading to an unsatisfactory viewing experience. A widely accepted paradigm is to create an error detection mechanism that identifies the cases when audio is leading or lagging. We propose ModEFormer, which independently extracts audio and video embeddings using modality-specific transformers. Different from the other transformer-based approaches, ModEFormer preserves the modality of the input streams which allows us to use a larger batch size with more negative audio samples for contrastive learning. Further, we propose a tradeoff between the number of negative samples and number of unique samples in a batch to significantly exceed the performance of previous methods. Experimental results show that ModEFormer achieves state-of-the-art performance, 94.5% for LRS2 and 90.9% for LRS3. Finally, we demonstrate how ModEFormer can be used for offset detection for test clips.

Dubbed video generation aims to accurately synchronize mouth movements of a given facial video with driving audio while preserving identity and scene-specific visual dynamics, such as head pose and lighting. Despite the accurate lip generation of previous approaches that adopts a pre-trained audio-video synchronization metric as an objective function, called Sync-Loss, extending it to high-resolution videos was challenging due to shift biases in the loss landscape that inhibit tandem optimization of Sync-Loss and visual quality, leading to a loss of detail.To address this issue, we introduce shift-invariant learning, which generates photo-realistic high-resolution videos with accurate Lip-Sync. Further, we employ a pyramid network with coarse-to-fine image generation to improve stability and lip syncronization. Our model outperforms state-of-the-art methods on multiple benchmark datasets, including AVSpeech, HDTF, and LRW, in terms of photo-realism, identity preservation, and Lip-Sync accuracy.