Data standardization for robust lip sync

Lip sync is a fundamental audio-visual task. However, existing lip sync methods fall short of being robust in the wild. One important cause could be distracting factors on the visual input side, making extracting lip motion information difficult. To address these issues, this paper proposes a data standardization pipeline to standardize the visual input for lip sync. Based on recent advances in 3D face reconstruction, we first create a model that can consistently disentangle lip motion information from the raw images. Then, standardized images are synthesized with disentangled lip motion information, with all other attributes related to distracting factors set to predefined values independent of the input, to reduce their effects. Using synthesized images, existing lip sync methods improve their data efficiency and robustness, and they achieve competitive performance for the active speaker detection task.