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Found 11 papers, 4 papers with code
Laughing Matters: Introducing Laughing-Face Generation using Diffusion Models
Speech-driven animation has gained significant traction in recent years, with current methods achieving near-photorealistic results.
SynthVSR: Scaling Up Visual Speech Recognition With Synthetic Supervision
Furthermore, when combined with large-scale pseudo-labeled audio-visual data SynthVSR yields a new state-of-the-art VSR WER of 16. 9% using publicly available data only, surpassing the recent state-of-the-art approaches trained with 29 times more non-public machine-transcribed video data (90, 000 hours).
Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation
Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos.
End-to-End Video-To-Speech Synthesis using Generative Adversarial Networks
In this work, we propose a new end-to-end video-to-speech model based on Generative Adversarial Networks (GANs) which translates spoken video to waveform end-to-end without using any intermediate representation or separate waveform synthesis algorithm.
DINO: A Conditional Energy-Based GAN for Domain Translation
Domain translation is the process of transforming data from one domain to another while preserving the common semantics.
Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection
Extensive experiments show that this simple approach significantly surpasses the state-of-the-art in terms of generalisation to unseen manipulations and robustness to perturbations, as well as shed light on the factors responsible for its performance.
Ranked #5 on
DeepFake Detection
on FakeAVCeleb
Visually Guided Self Supervised Learning of Speech Representations
Self supervised representation learning has recently attracted a lot of research interest for both the audio and visual modalities.
Ranked #8 on
Speech Emotion Recognition
on CREMA-D
Speech-driven facial animation using polynomial fusion of features
Speech-driven facial animation involves using a speech signal to generate realistic videos of talking faces.
Video-Driven Speech Reconstruction using Generative Adversarial Networks
Speech is a means of communication which relies on both audio and visual information.
Realistic Speech-Driven Facial Animation with GANs
We present an end-to-end system that generates videos of a talking head, using only a still image of a person and an audio clip containing speech, without relying on handcrafted intermediate features.
End-to-End Speech-Driven Facial Animation with Temporal GANs
To the best of our knowledge, this is the first method capable of generating subject independent realistic videos directly from raw audio.
