title: HunyuanCustom: A Multimodal-Driven Architecture for Customized Video
    Generation
summary: 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.
paper_authors._id: 681c546817fc8222efed5318
paper_authors.name: Teng Hu
paper_authors._id: 681c546817fc8222efed5319
paper_authors.name: Zhentao Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-09T08:35:37.319Z
paper_authors._id: 616e6f8a5d00995f3b51f0b5
paper_authors.avatarUrl: /avatars/b900e2215dd92e075a0ec2a8e78fd9fd.svg
paper_authors.fullname: zhentaoyu
paper_authors.type: user
paper_authors.user: zhentaoyu
paper_authors._id: 681c546817fc8222efed531a
paper_authors.name: Zhengguang Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-09T08:36:00.577Z
paper_authors._id: 675e96961d2fc5d068c4efc1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Acp1CQH6qhyMA7qCwozF6.png
paper_authors.fullname: Zhengguang Zhou
paper_authors.type: user
paper_authors.user: chroshimmer
paper_authors._id: 681c546817fc8222efed531b
paper_authors.name: Sen Liang
paper_authors._id: 681c546817fc8222efed531c
paper_authors.name: Yuan Zhou
paper_authors._id: 681c546817fc8222efed531d
paper_authors.name: Qin Lin
paper_authors._id: 681c546817fc8222efed531e
paper_authors.name: Qinglin Lu
paper_title: HunyuanCustom: A Multimodal-Driven Architecture for Customized Video
    Generation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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