Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion
  Dependency
Authors: name: Jianwen Jiang, name: Chao Liang, name: Jiaqi Yang, name: Gaojie Lin, name: Tianyun Zhong, name: Yanbo Zheng

With the introduction of diffusion-based video generation techniques,
audio-conditioned human video generation has recently achieved significant
breakthroughs in both the naturalness of motion and the synthesis of portrait
details. Due to the limited control of audio signals in driving human motion,
existing methods often add auxiliary spatial signals to stabilize movements,
which may compromise the naturalness and freedom of motion. In this paper, we
propose an end-to-end audio-only conditioned video diffusion model named Loopy.
Specifically, we designed an inter- and intra-clip temporal module and an
audio-to-latents module, enabling the model to leverage long-term motion
information from the data to learn natural motion patterns and improving
audio-portrait movement correlation. This method removes the need for manually
specified spatial motion templates used in existing methods to constrain motion
during inference. Extensive experiments show that Loopy outperforms recent
audio-driven portrait diffusion models, delivering more lifelike and
high-quality results across various scenarios.