  In the field of portrait video generation, the use of single images to
generate portrait videos has become increasingly prevalent. A common approach
involves leveraging generative models to enhance adapters for controlled
generation. However, control signals (e.g., text, audio, reference image, pose,
depth map, etc.) can vary in strength. Among these, weaker conditions often
struggle to be effective due to interference from stronger conditions, posing a
challenge in balancing these conditions. In our work on portrait video
generation, we identified audio signals as particularly weak, often
overshadowed by stronger signals such as facial pose and reference image.
However, direct training with weak signals often leads to difficulties in
convergence. To address this, we propose V-Express, a simple method that
balances different control signals through the progressive training and the
conditional dropout operation. Our method gradually enables effective control
by weak conditions, thereby achieving generation capabilities that
simultaneously take into account the facial pose, reference image, and audio.
The experimental results demonstrate that our method can effectively generate
portrait videos controlled by audio. Furthermore, a potential solution is
provided for the simultaneous and effective use of conditions of varying
strengths.
