Lidar data utilization for AI model training in filmmaking
View Patent ↗A method enhances AI model training in filmmaking using Lidar data to correlate 2D video with 3D spatial data, process filmmaking metadata, and simulate professional techniques. A system includes processors and memory to correlate video data with Lidar data, process filmmaking metadata, and train AI models for realistic video content generation. A computer-readable medium contains instructions for using Lidar and metadata to train AI models in simulating professional filmmaking techniques, enhancing video content realism.
1. A computer-implemented method for enhancing artificial intelligence (AI) model training in filmmaking through a use of Lidar data, the method comprising:
correlating two-dimensional video data with three-dimensional spatial data obtained from Lidar to simulate professional camera techniques;
receiving detailed metadata related to professional filmmaking techniques, including camera settings, shot composition, and lighting setups;
processing the received metadata alongside the Lidar data to provide one or more AI models with a granular understanding of spatial relationships and the physics of camera movement; and
training the AI models using the processed metadata and Lidar data to accurately simulate professional filmmaking techniques, thereby enhancing realism and quality of generated video content.
2. The method of claim 1 , further comprising generating synthetic data based on the processed metadata and Lidar data to provide the AI models with diverse scenarios for training without the need for new real-world video data.
3. The method of claim 1 , further comprising implementing continuous learning mechanisms that dynamically adjust the AI models based on structured feedback mechanisms, enabling iterative improvements in video content generation.
4. The method of claim 1 , further comprising utilizing machine learning techniques to analyze the correlation between the two-dimensional video data and the three-dimensional spatial data from Lidar, enabling the AI models to predict and replicate the impact of camera movements and positioning on a perceived depth and dimensionality of the scene.
5. The method of claim 1 , further comprising employing a data augmentation process that manipulates the Lidar data to simulate various environmental conditions and physical constraints encountered in real-world filmmaking, thereby broadening exposure of the AI models to different filming scenarios.
6. The method of claim 1 , further comprising integrating the trained AI models with a user interface that allows filmmakers to input specific filmmaking requirements and preferences, facilitating the generation of video content that closely aligns with individual creative visions and technical specifications.
7. The method of claim 1 , further comprising deploying the trained AI models in a cloud-based platform.
8. A computing system for enhancing artificial intelligence (AI) model training in filmmaking through use of Lidar data, the system comprising:
one or more processors; and
one or more memories having stored thereon computer-executable instructions that, when executed, cause the system to:
receive and process metadata related to professional filmmaking techniques;
correlate two-dimensional video data with three-dimensional spatial data obtained from Lidar; and
train one or more AI models using the processed metadata and Lidar data to accurately simulate professional filmmaking techniques.
9. The system of claim 8 , further comprising instructions that when executed, cause the system to generate synthetic data based on the processed metadata and Lidar data, providing the AI with diverse scenarios for training.
10. The system of claim 8 , further comprising instructions that when executed, cause the system to implement continuous learning mechanisms that dynamically adjust the AI models based on structured feedback mechanisms, enabling iterative improvements in video content generation.
11. The system of claim 8 , further comprising instructions that when executed, cause the system to use machine learning to analyze the correlation between the two-dimensional video data and the three-dimensional spatial data from Lidar, enabling the AI models to predict and replicate an impact of camera movements and positioning on a perceived depth and dimensionality of a scene.
12. The system of claim 8 , further comprising instructions that when executed, cause the system to employ a data augmentation process that manipulates the Lidar data to simulate various environmental conditions and physical constraints encountered in real-world filmmaking, thereby broadening exposure of the AI models to different filming scenarios.
13. The system of claim 8 , further comprising instructions that when executed, cause the system to integrate the trained AI models with a user interface that allows filmmakers to input specific filmmaking requirements and preferences, facilitating generation of video content that closely aligns with individual creative visions and technical specifications.
14. The system of claim 8 , further comprising instructions that when executed, cause the system to deploy the trained AI models in a cloud-based platform.
15. A non-transitory computer-readable medium having stored thereon instructions that when executed by a processor cause a system to perform:
correlating two-dimensional video data with three-dimensional spatial data obtained from Lidar;
receiving detailed metadata related to professional filmmaking techniques;
processing the received metadata alongside the Lidar data; and
training one or more artificial intelligence (AI) models using the processed metadata and Lidar data to accurately simulate professional filmmaking techniques, thereby enhancing the realism and quality of generated video content.
16. The non-transitory computer-readable medium of claim 15 , further comprising instructions that cause the system to generate synthetic data based on the processed metadata and Lidar data, providing the AI with diverse scenarios for training.
17. The non-transitory computer-readable medium of claim 15 , further comprising instructions that cause the system to implement continuous learning mechanisms that dynamically adjust the AI models based on structured feedback mechanisms, enabling iterative improvements in video content generation.
18. The non-transitory computer-readable medium of claim 15 , further comprising instructions that cause the system to employ a data augmentation process that manipulates the Lidar data to simulate various environmental conditions and physical constraints encountered in real-world filmmaking, thereby broadening the exposure of the AI models to different filming scenarios.
19. The non-transitory computer-readable medium of claim 15 , further comprising instructions that cause the system to integrate the trained AI models with a user interface that allows filmmakers to input specific filmmaking requirements and preferences, facilitating the generation of video content that closely aligns with individual creative visions and technical specifications.
20. The non-transitory computer-readable medium of claim 15 , further comprising instructions that cause the system to deploy the trained AI models in a cloud-based platform.