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Speech-driven facial video generation has been a complex problem due to itsmulti-modal aspects namely audio and video domain. The audio comprises lots ofunderlying features such as expression, pitch, loudness, prosody(speakingstyle) and facial video has lots of variability in terms of head movement, eyeblinks, lip synchronization and movements of various facial action units alongwith temporal smoothness. Synthesizing highly expressive facial videos from theaudio input and static image is still a challenging task for generativeadversarial networks. In this paper, we propose a multi-modal adaptivenormalization(MAN) based architecture to synthesize a talking person video ofarbitrary length using as input: an audio signal and a single image of aperson. The architecture uses the multi-modal adaptive normalization, keypointheatmap predictor, optical flow predictor and class activation map[58] basedlayers to learn movements of expressive facial components and hence generates ahighly expressive talking-head video of the given person. The multi-modaladaptive normalization uses the various features of audio and video such as Melspectrogram, pitch, energy from audio signals and predicted keypointheatmap/optical flow and a single image to learn the respective affineparameters to generate highly expressive video. Experimental evaluationdemonstrates superior performance of the proposed method as compared toRealistic Speech-Driven Facial Animation with GANs(RSDGAN) [53], Speech2Vid[10], and other approaches, on multiple quantitative metrics including: SSIM(structural similarity index), PSNR (peak signal to noise ratio), CPBD (imagesharpness), WER(word error rate), blinks/sec and LMD(landmark distance).Further, qualitative evaluation and Online Turing tests demonstrate theefficacy of our approach.

Multi Modal Adaptive Normalization for Audio to Video Generation