HunyuanCustom: A Multimodal-Driven Architecture for Customized Video
  Generation
Customized video generation aims to produce videos featuring specific
subjects under flexible user-defined conditions, yet existing methods often
struggle with identity consistency and limited input modalities. In this paper,
we propose HunyuanCustom, a multi-modal customized video generation framework
that emphasizes subject consistency while supporting image, audio, video, and
text conditions. Built upon HunyuanVideo, our model first addresses the
image-text conditioned generation task by introducing a text-image fusion
module based on LLaVA for enhanced multi-modal understanding, along with an
image ID enhancement module that leverages temporal concatenation to reinforce
identity features across frames. To enable audio- and video-conditioned
generation, we further propose modality-specific condition injection
mechanisms: an AudioNet module that achieves hierarchical alignment via spatial
cross-attention, and a video-driven injection module that integrates
latent-compressed conditional video through a patchify-based feature-alignment
network. Extensive experiments on single- and multi-subject scenarios
demonstrate that HunyuanCustom significantly outperforms state-of-the-art open-
and closed-source methods in terms of ID consistency, realism, and text-video
alignment. Moreover, we validate its robustness across downstream tasks,
including audio and video-driven customized video generation. Our results
highlight the effectiveness of multi-modal conditioning and identity-preserving
strategies in advancing controllable video generation. All the code and models
are available at https://hunyuancustom.github.io.
681c546817fc8222efed5318
Teng Hu
681c546817fc8222efed5319
Zhentao Yu
admin_assigned
2025-05-09T08:35:37.319Z
616e6f8a5d00995f3b51f0b5
/avatars/b900e2215dd92e075a0ec2a8e78fd9fd.svg
zhentaoyu
user
zhentaoyu
681c546817fc8222efed531a
Zhengguang Zhou
admin_assigned
2025-05-09T08:36:00.577Z
675e96961d2fc5d068c4efc1
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Acp1CQH6qhyMA7qCwozF6.png
Zhengguang Zhou
user
chroshimmer
681c546817fc8222efed531b
Sen Liang
681c546817fc8222efed531c
Yuan Zhou
681c546817fc8222efed531d
Qin Lin
681c546817fc8222efed531e
Qinglin Lu
HunyuanCustom: A Multimodal-Driven Architecture for Customized Video
  Generation
Customized video generation aims to produce videos featuring specific
subjects under flexible user-defined conditions, yet existing methods often
struggle with identity consistency and limited input modalities. In this paper,
we propose HunyuanCustom, a multi-modal customized video generation framework
that emphasizes subject consistency while supporting image, audio, video, and
text conditions. Built upon HunyuanVideo, our model first addresses the
image-text conditioned generation task by introducing a text-image fusion
module based on LLaVA for enhanced multi-modal understanding, along with an
image ID enhancement module that leverages temporal concatenation to reinforce
identity features across frames. To enable audio- and video-conditioned
generation, we further propose modality-specific condition injection
mechanisms: an AudioNet module that achieves hierarchical alignment via spatial
cross-attention, and a video-driven injection module that integrates
latent-compressed conditional video through a patchify-based feature-alignment
network. Extensive experiments on single- and multi-subject scenarios
demonstrate that HunyuanCustom significantly outperforms state-of-the-art open-
and closed-source methods in terms of ID consistency, realism, and text-video
alignment. Moreover, we validate its robustness across downstream tasks,
including audio and video-driven customized video generation. Our results
highlight the effectiveness of multi-modal conditioning and identity-preserving
strategies in advancing controllable video generation. All the code and models
are available at https://hunyuancustom.github.io.
HunyuanCustom is a multi-modal video generation framework that enhances identity consistency and handles various types of input conditions through specific fusion and injection mechanisms, outperforming existing methods.
multi-modal video generation identity consistency image-text fusion LLaVA image ID enhancement temporal concatenation AudioNet hierarchical alignment spatial cross-attention video-driven latent-compressed patchify feature-alignment text-video alignment