Given an arbitrary speech clip and a facial image, talking face generation aims to synthesize a talking face video with precise lip synchronization as well as a smooth transition of facial motion over the entire video speech. Most existing methods mainly focus on either disentangling the information in a single image or learning temporal information between frames. However, speech audio and video often have cross-modality coherence that has not been well addressed during synthesis. Therefore, this paper proposes a novel high-resolution talking face generation model for arbitrary person by discovering the cross-modality coherence via Mutual Information Approximation (MIA). By assuming the modality difference between audio and video is larger that of real video and generated video, we estimate mutual information between real audio and video, and then use a discriminator to enforce generated video distribution approach real video distribution. Furthermore, we introduce a dynamic attention technique on the mouth to enhance the robustness during the training stage. Experimental results on benchmark dataset LRW transcend the state-of-the-art methods on prevalent metrics with robustness on gender, pose variations and high-resolution synthesizing.
Earlier works on talking face generation mainly synthesize the specific identity from the dataset by given an arbitrary speech audio. use a time-delayed LSTM @cite_2 to generate key points synced to the audio and use another network to generate the video frames conditioned on the key points. Furthermore, propose a teeth proxy to improve the quality of the teeth during generation.
@cite_2
2883082281 2796931171 1779294284 2289286917
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. Given an arbitrary face image and an arbitrary speech clip, the proposed work attempts to generating the talking face video with accurate lip synchronization while maintaining smooth transition of both lip and facial movement over the entire video clip. Existing works either do not consider temporal dependency on face images across different video frames thus easily yielding noticeable abrupt facial and lip movement or are only limited to the generation of talking face video for a specific person thus lacking generalization capacity. We propose a novel conditional video generation network where the audio input is treated as a condition for the recurrent adversarial network such that temporal dependency is incorporated to realize smooth transition for the lip and facial movement. In addition, we deploy a multi-task adversarial training scheme in the context of video generation to improve both photo-realism and the accuracy for lip synchronization. Finally, based on the phoneme distribution information extracted from the audio clip, we develop a sample selection method that effectively reduces the size of the training dataset without sacrificing the quality of the generated video. Extensive experiments on both controlled and uncontrolled datasets demonstrate the superiority of the proposed approach in terms of visual quality, lip sync accuracy, and smooth transition of lip and facial movement, as compared to the state-of-the-art. This paper proposes a deep bidirectional long short-term memory approach in modeling the long contextual, nonlinear mapping between audio and visual streams for video-realistic talking head. In training stage, an audio-visual stereo database is firstly recorded as a subject talking to a camera. The audio streams are converted into acoustic feature, i.e. Mel-Frequency Cepstrum Coefficients (MFCCs), and their textual labels are also extracted. The visual streams, in particular, the lower face region, are compactly represented by active appearance model (AAM) parameters by which the shape and texture variations can be jointly modeled. Given pairs of the audio and visual parameter sequence, a DBLSTM model is trained to learn the sequence mapping from audio to visual space. For any unseen speech audio, whether it is original recorded or synthesized by text-to-speech (TTS), the trained DBLSTM model can predict a convincing AAM parameter trajectory for the lower face animation. To further improve the realism of the proposed talking head, the trajectory tiling method is adopted to use the DBLSTM predicted AAM trajectory as a guide to select a smooth real sample image sequence from the recorded database. We then stitch the selected lower face image sequence back to a background face video of the same subject, resulting in a video-realistic talking head. Experimental results show that the proposed DBLSTM approach outperforms the existing HMM-based approach in both objective and subjective evaluations. Talking heads synthesis with expressions from speech is proposed in this paper. Talking heads synthesis can be considered as a learning problem of sequence-to-sequence mapping, which consists of audio as input and video as output. To synthesize talking heads, we use SAVEE database which consists of videos of multiple sentences speeches recorded from front of face. Audiovisual data can be considered as two parallel sequential data of audio and visual features and it is composed of continuous value. Thus, audio and visual features of our dataset are represented by a regression model. In this research, the regression model is trained with long short-term memory (LSTM) by minimizing mean squared error (MSE). Then, audio features are used as input and visual features are used as target of LSTM. Thereby, talking heads are synthesized from speech. Our method is proposed to use lower level audio features than phonemes and it enables to synthesize talking heads with expressions while existing researches which use phonemes as audio features only can synthesize neutral expression talking heads. With SAVEE database, we achieved the minimum MSE 17.03 on our testing dataset. In experiment, we use mel-frequency cepstral coefficient (MFCC), AMFCC and A2 MFCC with energy as audio feature and active appearance model (AAM) on entire face region as visual feature.