| title | DreamX-Creator: Democratizing Native Audio-Video Generation at 2K Resolution |
|---|---|
| authors | Jiashu Zhu, Yanhao Zheng, Ruitian Tian, Rujing Dang, Shen Zhang, Bingze Song, Jiachen Lei, Ruimin Lin, Jiahong Wu, Xiangxiang Chu |
| date | 2026-08-31 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.31106 |
| affiliation | AMAP-ML |
| upvotes | 34 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Recent video generators often omit audio or synthesize it in a separate stage, limiting reciprocal modeling of visual dynamics and acoustic events. We present DreamX-Creator 1.0, a compact native joint audio-video generation system centered on a 7B generator. Conditioned on a first frame and a text prompt, the generator jointly denoises modality-specialized audio and video streams. The streams are processed independently in the first half of the network and coupled in the latter half through Gated Cross-Modal Attention, whose token- and head-wise output gates modulate each active cross-modal attention-head output. A unified Audio-Video Data System constructs and filters temporally coherent clips, produces structured multimodal annotations, and organizes clips into capability-oriented data pools. Progressive Joint Training comprises two audio-video pre-training stages followed by High-Quality Finetuning. Audio-Video Reinforcement Learning further post-trains the generator with Modality-Aware Multimodal Feedback that routes video-, audio-, and cross-modal feedback to the corresponding streams. For high-resolution output, our Autoregressive 1-Step 2K Refinement pipeline adapts a bidirectional multi-step teacher into an autoregressive multi-step refiner and distills it into a student requiring one denoising evaluation per temporal chunk. Overall, DreamX-Creator 1.0 achieves native, synchronized audio-video generation with performance competitive with state-of-the-art open-source systems. By releasing our compact 7B generator and 2K Refiner, we seek to democratize native audio-video generation and provide an accessible foundation for future research in unified audio-video generative modeling. |
cite
@misc{zhu2026dreamx,
title={DreamX-Creator: Democratizing Native Audio-Video Generation at 2K Resolution},
author={Jiashu Zhu and Yanhao Zheng and Ruitian Tian and Rujing Dang and Shen Zhang and Bingze Song and Jiachen Lei and Ruimin Lin and Jiahong Wu and Xiangxiang Chu},
year={2026},
eprint={2608.31106},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.31106}
}