

Cinemaster
Overview :
CineMaster is a framework focused on high-quality, cinematic video generation. Through 3D awareness and precise controllability, it empowers users to control object placement, camera movements, and the arrangement of rendered frames with the precision of professional film directors. The framework operates in two stages: The first stage involves an interactive workflow that allows users to intuitively construct conditional signals in 3D space. The second stage leverages these signals as guidance for a text-to-video diffusion model, generating the user's desired video content. CineMaster's key advantages lie in its high degree of controllability and 3D awareness, enabling the generation of high-quality, dynamic video content suitable for film production, advertising creation, and related fields.
Target Users :
CineMaster is suitable for filmmakers, advertising creatives, video creators, and others who require high-quality, customizable video generation tools for unique visual effects and creative expression. The framework's 3D awareness and controllability enable it to meet the exacting demands of professional users for high-quality video content.
Use Cases
Generate a video of a man flying to the moon.
Create a scene of a golden ship flying in the clouds.
Produce an animation of a dolphin flying towards the sun.
Features
Enables precise object placement in 3D space.
Offers flexible manipulation of object and camera movements.
Allows intuitive construction of 3D conditional signals through an interactive workflow.
Utilizes rendered depth maps, camera trajectories, and object category labels to guide video generation.
Provides automated data labeling processes to extract 3D bounding boxes and camera trajectories from large-scale video data.
How to Use
Visit the CineMaster project page to understand the framework's basic information and functionalities.
Utilize the interactive workflow to position object bounding boxes and define camera movements within a 3D space.
Input the generated control signals (e.g., depth maps, camera trajectories) into a text-to-video diffusion model.
Generate the desired video content based on user-provided text descriptions and control signals.
Review the demonstration examples provided on the page to observe video generation results in different scenarios.
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