US20260024241
2026-01-22
Physics
G06T11/00
The system described integrates event-driven video synthesis with textual descriptions, leveraging a novel framework called CUBE. This framework utilizes an event camera to capture asynchronous changes in light intensity, which are then processed by a text-to-image diffusion model. The model is conditioned on textual inputs to generate videos that are detailed and contextually accurate. An edge extraction module translates event data into a format suitable for the diffusion model, facilitating the synthesis of videos based on textual prompts.
The CUBE Plus system enhances the original framework by introducing a content frame identification module. This module selects the most information-rich segments of event data to drive cross-frame attention. Additionally, an event-driven attention mechanism is implemented to focus on moments dense with events, improving the synthesis of video content. These innovations address the limitations of traditional video generation methods, which often require extensive datasets and struggle with real-time dynamic inputs.
Event cameras differ from conventional cameras by recording changes in brightness at the pixel level asynchronously. This allows for high temporal resolution and a high dynamic range, making them suitable for capturing fast movements and challenging lighting conditions. However, event cameras do not record absolute intensity values, posing challenges in capturing textures and colors. The integration of these cameras with a diffusion model aims to overcome these limitations by allowing the synthesis of visually enriched content.
Traditional video reconstruction methods using event cameras have faced challenges such as noise accumulation and lack of realism. The integration of diffusion models offers improvements by sampling from distributions of possible reconstructions, achieving more realistic outputs. However, the inherent characteristics of event cameras still pose challenges, particularly in low-light conditions. The proposed system seeks to capitalize on the motion-detecting strengths of event cameras while enabling customizable and realistic video generation.
The system's ability to synthesize videos based on textual descriptions opens new possibilities in various fields. Applications include augmented reality/virtual reality (AR/VR), creative arts, autonomous driving, sports, surveillance, and robotics. By leveraging the unique capabilities of event cameras and diffusion models, the system offers a promising approach to generating controllable and contextually accurate video content, expanding the potential for innovative applications in dynamic and complex environments.