no code implementations • 5 May 2024 • Mengxian Hu, Minghao Zhu, Xun Zhou, Qingqing Yan, Shu Li, Chengju Liu, Qijun Chen
Motion diffusion models excel at text-driven motion generation but struggle with real-time inference since motion sequences are time-axis redundant and solving reverse diffusion trajectory involves tens or hundreds of sequential iterations.
no code implementations • 19 May 2023 • Liuyi Wang, Chengju Liu, Zongtao He, Shu Li, Qingqing Yan, Huiyi Chen, Qijun Chen
The experimental results demonstrate that PASTS outperforms all existing speaker models and successfully improves the performance of previous VLN models, achieving state-of-the-art performance on the standard Room-to-Room (R2R) dataset.
1 code implementation • 2 Mar 2023 • Zongtao He, Liuyi Wang, Shu Li, Qingqing Yan, Chengju Liu, Qijun Chen
For a better performance in continuous VLN, we design a multi-level instruction understanding procedure and propose a novel model, Multi-Level Attention Network (MLANet).