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Found 9 papers, 3 papers with code
Highly Detailed and Temporal Consistent Video Stylization via Synchronized Multi-Frame Diffusion
However, they struggle to generate videos with both highly detailed appearance and temporal consistency.
Text-Guided Texturing by Synchronized Multi-View Diffusion
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.
DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors
Animating a still image offers an engaging visual experience.
Redistributing the Precision and Content in 3D-LUT-based Inverse Tone-mapping for HDR/WCG Display
The latter requires more efficiency, thus the pre-calculated LUT (look-up table) has become a popular solution.
Video Colorization with Pre-trained Text-to-Image Diffusion Models
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
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.
Improved Diffusion-based Image Colorization via Piggybacked Models
Our key idea is to exploit the color prior knowledge in the pre-trained T2I diffusion model for realistic and diverse colorization.
Neural Recognition of Dashed Curves With Gestalt Law of Continuity
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
We also applied several state of the art reinforcement learning algorithms such as Dreamer, DrQ, and Plan2Explore in the real-world Tetris game environment.
