arXiv AI By Huimin Wang, Chang Xia, Leilei Ouyang, Yongqi Kang, Yu Fu, Yuqi Ouyang

AgentMV: A State-Guided Multi-Agent Framework for Budget-Aware Music Video Generation

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arXiv:2605. 10723v2 Announce Type: replace-cross Abstract: Generating a complete music video from a song requires more than synthesizing visually plausible clips for individual lyric prompts.

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arXiv AI
Jul 23

Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering

arXiv:2607. 20253v1 Announce Type: cross Abstract: In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descriptions, and musical attributes.

By Junyu Dai, Xinyue Fan, Weiqin Li, Xiangang Li, Yunjia Li, Bin Ma, Yukun Ma, Chongjia Ni, Yufei Shi, Haoxu Wang, Menglin Wu, Jianwei Yu, Huaicheng Zhang, Han Zhao, Shengkui Zhao, Haina Zhu
Hugging Face Trending Papers
Jul 22

Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering

In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descriptions, and musical attributes. The proposed framework supports three tasks: Lyrics-to-Song Generation, which generates complete songs from text descriptions, lyrics, and musical attributes; Instrumental Music Generation, which creates music without vocals; and Cover Song Generation, which reinterprets existing songs with different styles while preserving their melodic content.

arXiv AI
Aug 11

MusicLayout: Explicit Structural Planning for Controllable Text-to-Music Generation

arXiv:2608. 09035v1 Announce Type: cross Abstract: Text-to-music generation has advanced rapidly, but current systems still rely primarily on global text prompts, leaving the structural organization of generated music implicit and difficult to inspect, control, or revise before audio generation.

By Shuyu Li, Kejun Zhang, Jiahe Lei, Shulei Ji, Zihao Wang, Jiaxing Yu, Wanying Wu, Lei Wang
Hugging Face Trending Papers
Aug 12

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence

Modern black-box Image-to-Video (I2V) models offer powerful capabilities in automated content creation, yet their lack of fine-grained control and reliability presents significant challenges in professional workflows. Their inherent stochasticity causes minor variations in textual prompts or hyperparameters to yield drastically different outputs often necessitating inefficient, brute-force trial-and-error processes.