Artificial intelligence-based video content creation with predetermined styles
A method generates AI-based video content with a style by capturing scenes in various formats, applying alterations, and training AI with feedback for authenticity. A system includes processors and memory to capture scenes, apply alterations, construct datasets, and train AI for generating styled video content. A non-transitory computer-readable medium has instructions for capturing scenes, applying post-production alterations, and training AI to generate video content with a predetermined style.
1 . A computer-implemented method for constructing and training an artificial intelligence model configured to generate video content with a predetermined style, the method comprising:
capturing one or more control images corresponding to a scene using standard digital video as a baseline;
capturing one or more test images of the scene using different film formats to document visual effects;
applying post-production alterations to the captured footage;
constructing a training dataset that includes a variety of shots captured under varied lighting conditions;
training an AI model with paired comparisons to enable it to learn specific visual signatures;
reviewing footage generated by the AI model to assess its authenticity and using feedback to refine the model; and
optimizing learning cycles to enhance an efficiency of the training.
2 . The method of claim 1 , wherein capturing the test images using different film formats includes using formats such as 35 mm, 16 mm, 8 mm, and Super 8 mm.
3 . The method of claim 1 , wherein applying post-production alterations includes techniques like push processing and bleach bypass.
4 . The method of claim 1 , wherein constructing the training dataset includes shots such as tight face shots, medium shots, and wide shots.
5 . The method of claim 1 , wherein training the AI model involves using control versus modified footage for paired comparisons.
6 . The method of claim 1 , wherein reviewing the footage includes assessing adherence to expected filmic qualities.
7 . The method of claim 1 , wherein optimizing learning cycles involves scaling down data acquisition as the AI shows proficiency.
8 . A computing system for constructing and training an artificial intelligence model configured to generate video content with a predetermined style, the system comprising:
one or more processors; and
one or more memories having stored thereon instructions that when executed by the one or more processors, cause the system to:
capture one or more control images corresponding to a scene using digital video as a baseline;
capture one or more test images of the scene using different film formats to document visual effects;
apply post-production alterations to the captured footage;
construct a training dataset that includes a variety of shots captured under varied lighting conditions;
train an AI model with paired comparisons to enable it to learn specific visual signatures;
review footage generate by the AI model to assess its authenticity and use feedback to refine the model; and
optimize learning cycles to enhance an efficiency of the training.
9 . The system of claim 8 , wherein the instructions further cause the system to capture the test images using film formats such as 35 mm, 16 mm, 8 mm, and Super 8 mm.
10 . The system of claim 8 , wherein the instructions further cause the system to apply post-production alterations including techniques like push processing and bleach bypass.
11 . The system of claim 8 , wherein the instructions further cause the system to construct a training dataset including shots such as tight face shots, medium shots, and wide shots.
12 . The system of claim 8 , wherein the instructions further cause the system to train the AI using control versus modified footage for paired comparisons.
13 . The system of claim 8 , wherein the instructions further cause the system to review the footage to assess adherence to expected filmic qualities.
14 . The system of claim 8 , wherein the instructions further cause the system to optimize learning cycles by scaling down data acquisition as the AI shows proficiency.
15 . A non-transitory computer-readable medium having stored thereon instructions that when executed by one or more processors of a system, cause the system to perform a method for constructing and training an artificial intelligence model configured to generate video content with a predetermined style, the method comprising:
capturing one or more control images corresponding to a scene using digital video as a baseline;
capturing one or more test images using different film formats to document visual effects;
applying post-production alterations to the captured footage;
constructing a training dataset that includes a variety of shots captured under varied lighting conditions;
training an AI model with paired comparisons to enable it to learn specific visual signatures;
reviewing footage generated by the AI model to assess its authenticity and using feedback to refine the model; and
optimizing learning cycles to enhance an efficiency of the training.
16 . The computer-readable medium of claim 15 , wherein the instructions further cause the system to capture scenes using film formats such as 35 mm, 16 mm, 8 mm, and Super 8 mm.
17 . The computer-readable medium of claim 15 , wherein the instructions further cause the system to apply post-production alterations including techniques like push processing and bleach bypass.
18 . The computer-readable medium of claim 15 , wherein the instructions further cause the system to construct a training dataset including shots such as tight face shots, medium shots, and wide shots.
19 . The computer-readable medium of claim 15 , wherein the instructions further cause the system to train the AI using control versus modified footage for paired comparisons.
20 . The computer-readable medium of claim 15 , wherein
the instructions further cause the system to review the AI-generated footage to assess adherence to expected filmic qualities.