AudCast: Audio-Driven Human Video Generation by Cascaded Diffusion Transformers
This work addresses the challenge of generating coherent full-body human videos from audio for applications like virtual avatars and content creation, representing a domain-specific advancement.
The paper tackles the problem of generating holistic human videos from audio, addressing limitations of existing methods that focus only on facial movements. The proposed AudCast framework achieves high-fidelity results with temporal coherence and fine details in facial and hand movements.
Despite the recent progress of audio-driven video generation, existing methods mostly focus on driving facial movements, leading to non-coherent head and body dynamics. Moving forward, it is desirable yet challenging to generate holistic human videos with both accurate lip-sync and delicate co-speech gestures w.r.t. given audio. In this work, we propose AudCast, a generalized audio-driven human video generation framework adopting a cascade Diffusion-Transformers (DiTs) paradigm, which synthesizes holistic human videos based on a reference image and a given audio. 1) Firstly, an audio-conditioned Holistic Human DiT architecture is proposed to directly drive the movements of any human body with vivid gesture dynamics. 2) Then to enhance hand and face details that are well-knownly difficult to handle, a Regional Refinement DiT leverages regional 3D fitting as the bridge to reform the signals, producing the final results. Extensive experiments demonstrate that our framework generates high-fidelity audio-driven holistic human videos with temporal coherence and fine facial and hand details. Resources can be found at https://guanjz20.github.io/projects/AudCast.