Generating high fidelity human video with specified identities has attracted
significant attention in the content generation community. However, existing
techniques struggle to strike a balance between training efficiency and
identity preservation, either requiring tedious case-by-case finetuning or
usually missing the identity details in video generation process. In this
study, we present ID-Animator, a zero-shot human-video generation approach that
can perform personalized video generation given single reference facial image
without further training. ID-Animator inherits existing diffusion-based video
generation backbones with a face adapter to encode the ID-relevant embeddings
from learnable facial latent queries. To facilitate the extraction of identity
information in video generation, we introduce an ID-oriented dataset
construction pipeline, which incorporates decoupled human attribute and action
captioning technique from a constructed facial image pool. Based on this
pipeline, a random face reference training method is further devised to
precisely capture the ID-relevant embeddings from reference images, thus
improving the fidelity and generalization capacity of our model for ID-specific
video generation. Extensive experiments demonstrate the superiority of
ID-Animator to generate personalized human videos over previous models.
Moreover, our method is highly compatible with popular pre-trained T2V models
like animatediff and various community backbone models, showing high
extendability in real-world applications for video generation where identity
preservation is highly desired. Our codes and checkpoints will be released at
this https URL

提供单个人脸参考图像，无需进一步训练，ID-Animator 是一种零样本的人类视频生成方法，具有个性化视频生成能力。该方法采用面部适配器从可学习的面部潜在查询中编码与身份相关的嵌入，通过 ID 导向的数据集构建技术和随机面部参考训练方法，提高模型在身份特定视频生成方面的准确性和泛化能力，实验结果表明 ID-Animator 比之前的模型更优秀，适用于对身份保护要求很高的实际视频生成应用。