JavisDiT++: Unified Modeling and Optimization for Joint Audio-Video Generation
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915811280551936 |
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| author | Liu, Kai Zheng, Yanhao Wang, Kai Wu, Shengqiong Zhang, Rongjunchen Luo, Jiebo Hatzinakos, Dimitrios Liu, Ziwei Fei, Hao Chua, Tat-Seng |
| author_facet | Liu, Kai Zheng, Yanhao Wang, Kai Wu, Shengqiong Zhang, Rongjunchen Luo, Jiebo Hatzinakos, Dimitrios Liu, Ziwei Fei, Hao Chua, Tat-Seng |
| contents | AIGC has rapidly expanded from text-to-image generation toward high-quality multimodal synthesis across video and audio. Within this context, joint audio-video generation (JAVG) has emerged as a fundamental task that produces synchronized and semantically aligned sound and vision from textual descriptions. However, compared with advanced commercial models such as Veo3, existing open-source methods still suffer from limitations in generation quality, temporal synchrony, and alignment with human preferences. To bridge the gap, this paper presents JavisDiT++, a concise yet powerful framework for unified modeling and optimization of JAVG. First, we introduce a modality-specific mixture-of-experts (MS-MoE) design that enables cross-modal interaction efficacy while enhancing single-modal generation quality. Then, we propose a temporal-aligned RoPE (TA-RoPE) strategy to achieve explicit, frame-level synchronization between audio and video tokens. Besides, we develop an audio-video direct preference optimization (AV-DPO) method to align model outputs with human preference across quality, consistency, and synchrony dimensions. Built upon Wan2.1-1.3B-T2V, our model achieves state-of-the-art performance merely with around 1M public training entries, significantly outperforming prior approaches in both qualitative and quantitative evaluations. Comprehensive ablation studies have been conducted to validate the effectiveness of our proposed modules. All the code, model, and dataset are released at https://JavisVerse.github.io/JavisDiT2-page. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_19163 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | JavisDiT++: Unified Modeling and Optimization for Joint Audio-Video Generation Liu, Kai Zheng, Yanhao Wang, Kai Wu, Shengqiong Zhang, Rongjunchen Luo, Jiebo Hatzinakos, Dimitrios Liu, Ziwei Fei, Hao Chua, Tat-Seng Computer Vision and Pattern Recognition Multimedia Sound AIGC has rapidly expanded from text-to-image generation toward high-quality multimodal synthesis across video and audio. Within this context, joint audio-video generation (JAVG) has emerged as a fundamental task that produces synchronized and semantically aligned sound and vision from textual descriptions. However, compared with advanced commercial models such as Veo3, existing open-source methods still suffer from limitations in generation quality, temporal synchrony, and alignment with human preferences. To bridge the gap, this paper presents JavisDiT++, a concise yet powerful framework for unified modeling and optimization of JAVG. First, we introduce a modality-specific mixture-of-experts (MS-MoE) design that enables cross-modal interaction efficacy while enhancing single-modal generation quality. Then, we propose a temporal-aligned RoPE (TA-RoPE) strategy to achieve explicit, frame-level synchronization between audio and video tokens. Besides, we develop an audio-video direct preference optimization (AV-DPO) method to align model outputs with human preference across quality, consistency, and synchrony dimensions. Built upon Wan2.1-1.3B-T2V, our model achieves state-of-the-art performance merely with around 1M public training entries, significantly outperforming prior approaches in both qualitative and quantitative evaluations. Comprehensive ablation studies have been conducted to validate the effectiveness of our proposed modules. All the code, model, and dataset are released at https://JavisVerse.github.io/JavisDiT2-page. |
| title | JavisDiT++: Unified Modeling and Optimization for Joint Audio-Video Generation |
| topic | Computer Vision and Pattern Recognition Multimedia Sound |
| url | https://arxiv.org/abs/2602.19163 |