NVIDIA CORPORATION (20240304177). EMOTION AND CHARACTER PARAMETERS FOR DIFFUSION MODEL CONTENT GENERATION SYSTEMS AND APPLICATIONS simplified abstract
Contents
EMOTION AND CHARACTER PARAMETERS FOR DIFFUSION MODEL CONTENT GENERATION SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
Hideaki Tagami of Yokohama (JP)
EMOTION AND CHARACTER PARAMETERS FOR DIFFUSION MODEL CONTENT GENERATION SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240304177 titled 'EMOTION AND CHARACTER PARAMETERS FOR DIFFUSION MODEL CONTENT GENERATION SYSTEMS AND APPLICATIONS
The approaches presented in this patent application provide systems and methods for generating three-dimensional (3D) content with fine-grained emotions and character traits. A set of classifiers is used to identify emotions and character traits from user input, expanding seed words through methods like manual collection, synonym extension, and word alignment. Inputs are evaluated for emotion and character traits by identifying specific words or phrases present. Output vectors associated with identified emotions and character traits are then provided to generative models to adjust content, such as modifying output audio or facial expressions for digital character representations.
- Systems and methods for generating 3D content with detailed emotions and character traits
- Use of classifiers to identify emotions and character traits from user input
- Expansion of seed words through manual collection, synonym extension, and word alignment
- Evaluation of inputs for indications of emotion and character traits
- Adjustment of content based on identified emotions and character traits for digital character representations
Potential Applications: This technology could be used in virtual reality applications, video games, animated films, and virtual assistants to create more realistic and emotionally expressive characters.
Problems Solved: This technology addresses the challenge of creating 3D content with nuanced emotions and character traits, enhancing user engagement and immersion in virtual environments.
Benefits: Enhanced user experience, more realistic and emotionally expressive digital characters, improved storytelling capabilities in virtual environments.
Commercial Applications: Title: "Enhancing User Engagement with Emotionally Expressive 3D Content" This technology could be commercially applied in the entertainment industry, virtual reality gaming, virtual assistant development, and animated film production to create more engaging and immersive experiences for users.
Prior Art: Researchers and developers in the fields of artificial intelligence, natural language processing, and computer graphics may have explored similar techniques for generating emotionally expressive 3D content.
Frequently Updated Research: Stay informed about advancements in artificial intelligence, natural language processing, and computer graphics to enhance the capabilities of generating emotionally expressive 3D content.
Questions about the Technology: 1. How does this technology improve user engagement in virtual environments? This technology enhances user engagement by creating more realistic and emotionally expressive digital characters, making interactions in virtual environments more immersive and engaging.
2. What are the potential applications of this technology beyond entertainment? This technology could also be applied in fields like virtual therapy, education, and customer service to create more engaging and personalized experiences for users.
Original Abstract Submitted
approaches presented herein provide systems and methods for generating three-dimensional (3d) content with fine grained emotions and character traits. a set of classifiers may be used to identify emotions and character traits from an input provided by a user. each of the classifiers in the set of classifiers may use a set of seed words that is expanded through methods including manual collection, synonym extension, and/or word alignment. an input may then be evaluated for indications of emotion and/or character traits, such as by identifying certain words or phrases present within the input. output vectors associated with the identified emotion and/or character traits may then be provided to different generative models to adjust content, such as modifications to output audio or facial expressions for digital character representations.
