no code implementations • 14 Dec 2024 • Pin-Yen Chiu, Dai-Jie Wu, Po-Hsun Chu, Chia-Hsuan Hsu, Hsiang-Chen Chiu, Chih-Yu Wang, Jun-Cheng Chen
To address these issues, we propose the Style Latent Diffusion Transformer (StyleDiT), a novel framework that integrates the strengths of StyleGAN with the diffusion model to generate high-quality and diverse kinship faces.
no code implementations • 7 Oct 2024 • Bo-Ruei Huang, Chun-Kai Yang, Chun-Mao Lai, Dai-Jie Wu, Shao-Hua Sun
Motivated by the recent success of diffusion models in generative modeling, we propose to integrate a diffusion model into the adversarial imitation learning from observation framework.
1 code implementation • 1 Jul 2024 • Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, Anjie Yang, Zhaoxuan Jin, Jianbo Deng, Philip Torr, Bernard Ghanem, Guohao Li
Leveraging Crab, we developed a cross-platform Crab Benchmark-v0 comprising 120 tasks in computer desktop and mobile phone environments.
no code implementations • 19 Sep 2023 • Jingyu Zhang, Huitong Yang, Dai-Jie Wu, Jacky Keung, Xuesong Li, Xinge Zhu, Yuexin Ma
Current state-of-the-art point cloud-based perception methods usually rely on large-scale labeled data, which requires expensive manual annotations.