OmniCustom: Sync Audio-Video Customization Via Joint Audio-Video Generation Model

• URL: http://arxiv.org/abs/2602.12304v1
• Date: Thu, 12 Feb 2026 03:25:41 GMT
• Title: OmniCustom: Sync Audio-Video Customization Via Joint Audio-Video Generation Model
• Authors: Maomao Li, Zhen Li, Kaipeng Zhang, Guosheng Yin, Zhifeng Li, Dong Xu,
• Abstract summary: We propose sync audio-video customization, which aims to synchronously customize both video identity and audio timbre.<n>This novel task requires generating videos that maintain the identity of the reference image while imitating the timbre of the reference audio.<n>To this end, we propose OmniCustom, a powerful DiT-based audio-video customization framework.
• Score: 37.45649985771698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing mainstream video customization methods focus on generating identity-consistent videos based on given reference images and textual prompts. Benefiting from the rapid advancement of joint audio-video generation, this paper proposes a more compelling new task: sync audio-video customization, which aims to synchronously customize both video identity and audio timbre. Specifically, given a reference image $I^{r}$ and a reference audio $A^{r}$, this novel task requires generating videos that maintain the identity of the reference image while imitating the timbre of the reference audio, with spoken content freely specifiable through user-provided textual prompts. To this end, we propose OmniCustom, a powerful DiT-based audio-video customization framework that can synthesize a video following reference image identity, audio timbre, and text prompts all at once in a zero-shot manner. Our framework is built on three key contributions. First, identity and audio timbre control are achieved through separate reference identity and audio LoRA modules that operate through self-attention layers within the base audio-video generation model. Second, we introduce a contrastive learning objective alongside the standard flow matching objective. It uses predicted flows conditioned on reference inputs as positive examples and those without reference conditions as negative examples, thereby enhancing the model ability to preserve identity and timbre. Third, we train OmniCustom on our constructed large-scale, high-quality audio-visual human dataset. Extensive experiments demonstrate that OmniCustom outperforms existing methods in generating audio-video content with consistent identity and timbre fidelity.

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