Pose-controllable character video generation is in high demand with extensive
applications for fields such as automatic advertising and content creation on
social media platforms. While existing character image animation methods using
pose sequences and reference images have shown promising performance, they tend
to struggle with incoherent animation in complex scenarios, such as multiple
character animation and body occlusion. Additionally, current methods request
large-scale high-quality videos with stable backgrounds and temporal
consistency as training datasets, otherwise, their performance will greatly
deteriorate. These two issues hinder the practical utilization of character
image animation tools. In this paper, we propose a practical and robust
framework Follow-Your-Pose v2, which can be trained on noisy open-sourced
videos readily available on the internet. Multi-condition guiders are designed
to address the challenges of background stability, body occlusion in
multi-character generation, and consistency of character appearance. Moreover,
to fill the gap of fair evaluation of multi-character pose animation, we
propose a new benchmark comprising approximately 4,000 frames. Extensive
experiments demonstrate that our approach outperforms state-of-the-art methods
by a margin of over 35\% across 2 datasets and on 7 metrics. Meanwhile,
qualitative assessments reveal a significant improvement in the quality of
generated video, particularly in scenarios involving complex backgrounds and
body occlusion of multi-character, suggesting the superiority of our approach.

提出了一个实用而稳健的框架 Follow-Your-Pose v2，它可以在互联网上易于获取的噪声数据集进行训练，并通过设计多条件引导器解决背景稳定性、多角色生成中的身体遮挡以及角色外貌的一致性等问题。通过对接近 4000 帧的新基准进行广泛实验，表明我们的方法在两个数据集和 7 个度量标准上比最先进的方法提高了 35% 以上。同时，定性评估表明我们的方法在生成复杂背景和多角色身体遮挡的场景中显著改善了生成视频的质量，表明了我们方法的优越性。