Talking face generation aims to synthesize a sequence of face images that correspond to a clip of speech. This is a challenging task because face appearance variation and semantics of speech are coupled together in the subtle movements of the talking face regions. Existing works either construct specific face appearance model on specific subjects or model the transformation between lip motion and speech. In this work, we integrate both aspects and enable arbitrary-subject talking face generation by learning disentangled audio-visual representation. We find that the talking face sequence is actually a composition of both subject-related information and speech-related information. These two spaces are then explicitly disentangled through a novel associative-and-adversarial training process. This disentangled representation has an advantage where both audio and video can serve as inputs for generation. Extensive experiments show that the proposed approach generates realistic talking face sequences on arbitrary subjects with much clearer lip motion patterns than previous work. We also demonstrate the learned audio-visual representation is extremely useful for the tasks of automatic lip reading and audio-video retrieval.
For the correspondence between human faces and audio clips, a number of works have been proposed to solve the problem of the audio-video synchronization between mouth motion and speech @cite_24 @cite_31 . Particularly, SyncNet @cite_31 @cite_23 used two stream CNNs to sync audio with 5 consecutive frames. @cite_23 , they further fixed the sync image feature as the pretraining for lip reading, but the two tasks are still separate from each other. Recently, works from @cite_11 @cite_7 also attempt to learn the association between a human face and voice for identity recognition instead of semantic level synchronization.
@cite_7 @cite_24 @cite_23 @cite_31 @cite_11
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We propose and investigate an identity sensitive joint embedding of face and voice. Such an embedding enables cross-modal retrieval from voice to face and from face to voice. We make the following four contributions: first, we show that the embedding can be learnt from videos of talking faces, without requiring any identity labels, using a form of cross-modal self-supervision; second, we develop a curriculum learning schedule for hard negative mining targeted to this task, that is essential for learning to proceed successfully; third, we demonstrate and evaluate cross-modal retrieval for identities unseen and unheard during training over a number of scenarios and establish a benchmark for this novel task; finally, we show an application of using the joint embedding for automatically retrieving and labelling characters in TV dramas. Lip synchronization is the determination of the motion of the mouth and tongue during speech. It can be deduced from the speech signal without phonemic analysis, and irrespective of the content of the speech. Our method is based on the observation that the position of the mouth over a short interval of time can be correlated with the basic shape of the spectrum of the speech over that same interval. The spectrum is obtained from a Fast Fourier Transform (FFT) and treated like a discrete probability density function. Statistical measures called moments are used to describe the shape. For several canonical utterances, video measurements of a speaker's mouth are combined with the corresponding moments to produce continuous predictor surfaces for each of three mouth parameters: jaw position, horizontal opening between the lips and vertical opening between the lips. The method involves smoothing so it is independent of the local behavior of the spectrum. The goal of this work is to determine the audio-video synchronisation between mouth motion and speech in a video. We introduce a seemingly impossible task: given only an audio clip of someone speaking, decide which of two face images is the speaker. In this paper we study this, and a number of related cross-modal tasks, aimed at answering the question: how much can we infer from the voice about the face and vice versa? We study this task "in the wild", employing the datasets that are now publicly available for face recognition from static images (VGGFace) and speaker identification from audio (VoxCeleb). These provide training and testing scenarios for both static and dynamic testing of cross-modal matching. We make the following contributions: (i) we introduce CNN architectures for both binary and multi-way cross-modal face and audio matching, (ii) we compare dynamic testing (where video information is available, but the audio is not from the same video) with static testing (where only a single still image is available), and (iii) we use human testing as a baseline to calibrate the difficulty of the task. We show that a CNN can indeed be trained to solve this task in both the static and dynamic scenarios, and is even well above chance on 10-way classification of the face given the voice. The CNN matches human performance on easy examples (e.g. different gender across faces) but exceeds human performance on more challenging examples (e.g. faces with the same gender, age and nationality).