Media2Face is a breakthrough multi-modal 3D facial animation generation model that can generate realistic facial expression animations based on multi-modal inputs such as speech. This model builds a large-scale data set named M2F-D by introducing Generalized Neural Parameterized Facial Assets (GNPFA) and using it to extract high-quality expression and head pose information from massive video data. Finally, the research team proposed a diffusion model Media2Face based on the GNPFA latent space, achieving high-quality co-language facial animation generation and reaching new heights in terms of fidelity and expressiveness. It allows users to personalize the generated animations, such as adjusting anger, happiness and other emotional expressions.
Media2Face is a product model that supports voice and other multi-modal guidance to generate 3D facial dynamic expressions. By making more detailed personalized adjustments to the generated facial animation, it also allows users to make more detailed personalized adjustments to the generated facial animation, such as anger, happiness, etc. The research team responded to the challenge through three key steps, first introducing generalized neural parameterized facial assets, then using GNPFA to extract high-quality expressions and accurate head poses from a large number of videos to form the M2F-D dataset, and finally proposed Media2Face, a GNPFA latent space-based diffusion model for co-language facial animation generation. Overall, Media2Face has achieved impressive results in the field of co-language facial animation, opening up new possibilities for the fidelity and expressiveness of facial animation synthesis.The emergence of the Media2Face model has brought new technological breakthroughs to the fields of 3D animation production, virtual reality, and human-computer interaction. Its efficient generation capabilities and highly personalized customization options herald a more realistic and expressive future. The arrival of digital characters. This technology has a wide range of application scenarios in the future and deserves continued attention to its development.