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@@ -35,7 +35,7 @@ Chong Ma, **Hanqi Jiang**, Wenting Chen, Yiwei Li, Zihao Wu, Xiaowei Yu, Zhengli
 Yiwei Li, Sekeun Kim, Zihao Wu, **Hanqi Jiang**, Yi Pan, Pengfei Jin, Sifan Song, Yucheng Shi, Xiaowei Yu, Tianze Yang, Tianming Liu, Quanzheng Li, Xiang Li
 
 <!-- [**Project**](https://speechresearch.github.io/fastspeech2/) <strong><span class='show_paper_citations' data='4FA6C0AAAAAJ:LkGwnXOMwfcC'></span></strong> -->
-  - Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily relies on manual expertise, leading to inconsistent results from clinical and portable devices. ECHO video generation offers a solution by improving automated monitoring through synthetic data and generating high-quality videos from routine health data. However, existing models often face high computational costs, slow inference, and rely on complex conditional prompts that require experts' annotations. To address these challenges, we propose \projectname{}, an ECG-conditioned ECHO video generation model. \projectname{} introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token modeling for fast decoding, and (2) it conditions on readily accessible ECG signals, which are highly coherent with ECHO videos, bypassing complex conditional prompts. To the best of our knowledge, this is the first work to use time-series prompts like ECG signals for ECHO video generation. \projectname{} not only enables controllable synthetic ECHO data generation but also provides updated cardiac function information for disease monitoring and prediction beyond ECG alone. Evaluations on three public and private datasets demonstrate state-of-the-art performance in ECHO video generation across both qualitative and quantitative measures. Additionally, \projectname{} can be easily generalized to other modality generation tasks, such as cardiac MRI, fMRI, and 3D CT generation. 
+  - Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily relies on manual expertise, leading to inconsistent results from clinical and portable devices. ECHO video generation offers a solution by improving automated monitoring through synthetic data and generating high-quality videos from routine health data. However, existing models often face high computational costs, slow inference, and rely on complex conditional prompts that require experts' annotations. To address these challenges, we propose \projectname{}, an ECG-conditioned ECHO video generation model. \projectname{} introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token modeling for fast decoding, and (2) it conditions on readily accessible ECG signals, which are highly coherent with ECHO videos, bypassing complex conditional prompts. To the best of our knowledge, this is the first work to use time-series prompts like ECG signals for ECHO video generation. ECHOPluse not only enables controllable synthetic ECHO data generation but also provides updated cardiac function information for disease monitoring and prediction beyond ECG alone. Evaluations on three public and private datasets demonstrate state-of-the-art performance in ECHO video generation across both qualitative and quantitative measures. Additionally, \projectname{} can be easily generalized to other modality generation tasks, such as cardiac MRI, fMRI, and 3D CT generation. 
   - [Demo](https://github.com/levyisthebest/ECHOPulse_Prelease)
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