Search Results for author: Hanyuan Liu

Found 10 papers, 3 papers with code

ToonCrafter: Generative Cartoon Interpolation

no code implementations28 May 2024 Jinbo Xing, Hanyuan Liu, Menghan Xia, Yong Zhang, Xintao Wang, Ying Shan, Tien-Tsin Wong

We introduce ToonCrafter, a novel approach that transcends traditional correspondence-based cartoon video interpolation, paving the way for generative interpolation.


Text-Guided Texturing by Synchronized Multi-View Diffusion

no code implementations21 Nov 2023 Yuxin Liu, Minshan Xie, Hanyuan Liu, Tien-Tsin Wong

In this paper, we propose a synchronized multi-view diffusion approach that allows the diffusion processes from different views to reach a consensus of the generated content early in the process, and hence ensures the texture consistency.


Video Colorization with Pre-trained Text-to-Image Diffusion Models

no code implementations2 Jun 2023 Hanyuan Liu, Minshan Xie, Jinbo Xing, Chengze Li, Tien-Tsin Wong

In this paper, we present ColorDiffuser, an adaptation of a pre-trained text-to-image latent diffusion model for video colorization.


Make-Your-Video: Customized Video Generation Using Textual and Structural Guidance

no code implementations1 Jun 2023 Jinbo Xing, Menghan Xia, Yuxin Liu, Yuechen Zhang, Yong Zhang, Yingqing He, Hanyuan Liu, Haoxin Chen, Xiaodong Cun, Xintao Wang, Ying Shan, Tien-Tsin Wong

Our method, dubbed Make-Your-Video, involves joint-conditional video generation using a Latent Diffusion Model that is pre-trained for still image synthesis and then promoted for video generation with the introduction of temporal modules.

Image Generation Video Generation

Improved Diffusion-based Image Colorization via Piggybacked Models

1 code implementation21 Apr 2023 Hanyuan Liu, Jinbo Xing, Minshan Xie, Chengze Li, Tien-Tsin Wong

Our key idea is to exploit the color prior knowledge in the pre-trained T2I diffusion model for realistic and diverse colorization.

Colorization Image Colorization

Neural Recognition of Dashed Curves With Gestalt Law of Continuity

no code implementations CVPR 2022 Hanyuan Liu, Chengze Li, Xueting Liu, Tien-Tsin Wong

While humans can intuitively recognize dashed curves from disjoint curve segments based on the law of continuity in Gestalt psychology, it is extremely difficult for computers to model the Gestalt law of continuity and recognize the dashed curves since high-level semantic understanding is needed for this task.

Learn to Play Tetris with Deep Reinforcement Learning

no code implementations CUHK Course IERG5350 2020 Hanyuan Liu, Lixin Liu

We also applied several state of the art reinforcement learning algorithms such as Dreamer, DrQ, and Plan2Explore in the real-world Tetris game environment.

Imitation Learning reinforcement-learning +1

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