📝 Publications

BATON: Aligning Text-to-Audio Model with Human Preference Feedback
Huan Liao^★, Haonan Han^★, Kai Yang, Tianjiao Du, Rui Yang, Zunnan Xu, Qinmei Xu, Jingquan Liu, Jiasheng Lu, Xiu Li^†

[Project] [Paper] [Dataset&Code]

• The first text-to-audio (TTA) system finetuned from human preference feedback.
• Curated a dataset containing both prompts and the corresponding generated audio, annotated based on human feedback.
• Addressed the audio event semantic omission and temporal disarray with a weighted preference strategy

Controllable Text-to-Audio Generation with Training-Free Temporal Guidance Diffusion
Tianjiao Du, Jun Chen, Jiasheng Lu, Qinmei Xu, Huan Liao, Yupeng Chen, Zhiyong Wu^†

[Paper]

• Training-free approach for controllable TTA generation based on the location and duration of corresponding sound events.

Rhythmic Foley: A Framework for Seamless Audio-Visual Alignment in Video-to-Audio Synthesis
Zhiqi Huang^★ Dan Luo^★ Jun Wang^† Huan Liao Zhiheng Li^† Zhiyong Wu^†

[Project] [Paper]

• An innovative framework for video-to-audio synthesis, characterized by semantic integrity and precise beat point synchronization.

REPARO: Compositional 3D Assets Generation with Differentiable 3D Layout Alignment
Haonan Han^★, Rui Yang^★, Huan Liao^★, Jiankai Xing, Zunnan Xu, Xiaoming Yu, Junwei Zha, Xiu Li^†, Wanhua Li^†

[Project] [Paper] [Code]

• A novel approach for compositional 3D asset generation from single images.

AToM: Aligning Text-to-Motion Model at Event-Level with GPT-4Vision Reward
Haonan Han^★, Xiangzuo Wu^★, Huan Liao^★, Zunnan Xu, Ronghui Li, Yachao Zhang^†, Xiu Li^†

[Project] [Paper] [Code]

• Enhances the event-level alignment between generated motion and text prompts by leveraging reward from GPT-4Vision.