2334192
Project Grant
Overview
Grant Description
Sbir Phase I: Artificial Intelligence for Automated Custom Avatar Creation -this small business innovation research (SBIR) Phase I project will create a way for experienced animators to rapidly make high quality three-dimensional (3D) content, and for novices to create engaging 3D content without the need for years of technical training or powerful but expensive software.
Engaging computer graphic content has revolutionized education, entertainment, medical, and virtual environments. This project will use artificial intelligence (AI) to unlock the full potential of 3D graphical content, a $17.21 billion annual market, by alleviating major bottlenecks in the workflow. While there are more than 62,000 animators currently employed in the United States, fewer than 10,000 work specifically at 3D animation studios, and a smaller proportion of them possess the skills for weight painting.
Weight painting is a vital technique in 3D design that adds realism to characters, enabling them to move smoothly during animation. Compounding the difficulty, weight painting 3D models is a tedious task that can take an expert up to 2 days (or around 16 work hours) to manually complete one model. Smaller animation shops often do not have the expertise to perform this task at all and are unable to compete for bigger, more lucrative contracts.
Furthermore, researchers and students at universities around the world are often unable to perform this weighting task, which reduces their ability to create animations for medical, athletic, and entertainment uses in augmented reality or virtual reality. This small business innovation research (SBIR) Phase I project will utilize deep neural networks (DNN) to create 3D models from text input as well as a weight-painted rig from an industry-standard skeleton system and a 3D model mesh.
The technology converts the mesh and skeleton into a format that can be processed by machine learning (ML) code, introducing a brand-new data structure. Additionally, the project will explore an adaptation of the COO (coordinate list) matrix, a sparse matrix that performs effectively with neural networks but faces challenges when applied to machine learning tasks in 3D space where coordinate ordering is uncertain.
The most difficult issues, such as weight painting and modeling, have been hampered by four specific limitations: 1. Lack of ground-truth, 2. Limited training data, 3. Lack of a-priori ML architecture, and 4. Lack of robustness and specificity for non-Gaussian data. This project will make inroads into each of these areas by establishing a methodology for incorporating and transforming non-Gaussian data for DNN analysis and will create a comprehensive data training set while establishing a domain specific ground-truth based on the canonical Turing test.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- Subawards are not planned for this award.
Engaging computer graphic content has revolutionized education, entertainment, medical, and virtual environments. This project will use artificial intelligence (AI) to unlock the full potential of 3D graphical content, a $17.21 billion annual market, by alleviating major bottlenecks in the workflow. While there are more than 62,000 animators currently employed in the United States, fewer than 10,000 work specifically at 3D animation studios, and a smaller proportion of them possess the skills for weight painting.
Weight painting is a vital technique in 3D design that adds realism to characters, enabling them to move smoothly during animation. Compounding the difficulty, weight painting 3D models is a tedious task that can take an expert up to 2 days (or around 16 work hours) to manually complete one model. Smaller animation shops often do not have the expertise to perform this task at all and are unable to compete for bigger, more lucrative contracts.
Furthermore, researchers and students at universities around the world are often unable to perform this weighting task, which reduces their ability to create animations for medical, athletic, and entertainment uses in augmented reality or virtual reality. This small business innovation research (SBIR) Phase I project will utilize deep neural networks (DNN) to create 3D models from text input as well as a weight-painted rig from an industry-standard skeleton system and a 3D model mesh.
The technology converts the mesh and skeleton into a format that can be processed by machine learning (ML) code, introducing a brand-new data structure. Additionally, the project will explore an adaptation of the COO (coordinate list) matrix, a sparse matrix that performs effectively with neural networks but faces challenges when applied to machine learning tasks in 3D space where coordinate ordering is uncertain.
The most difficult issues, such as weight painting and modeling, have been hampered by four specific limitations: 1. Lack of ground-truth, 2. Limited training data, 3. Lack of a-priori ML architecture, and 4. Lack of robustness and specificity for non-Gaussian data. This project will make inroads into each of these areas by establishing a methodology for incorporating and transforming non-Gaussian data for DNN analysis and will create a comprehensive data training set while establishing a domain specific ground-truth based on the canonical Turing test.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- Subawards are not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Athens,
Georgia
30606-6132
United States
Geographic Scope
Single Zip Code
Artimatic Technologies was awarded
Project Grant 2334192
worth $275,000
from National Science Foundation in December 2023 with work to be completed primarily in Athens Georgia United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I: Artificial Intelligence for Automated Custom Avatar Creation
Abstract
This Small Business Innovation Research (SBIR) Phase I project will create a way for experienced animators to rapidly make high quality three-dimensional (3D) content, and for novices to create engaging 3D content without the need for years of technical training or powerful but expensive software. Engaging computer graphic content has revolutionized education, entertainment, medical, and virtual environments. This project will use artificial intelligence (AI) to unlock the full potential of 3D graphical content, a $17.21 billion annual market, by alleviating major bottlenecks in the workflow. While there are more than 62,000 animators currently employed in the United States, fewer than 10,000 work specifically at 3D animation studios, and a smaller proportion of them possess the skills for weight painting. Weight painting is a vital technique in 3D design that adds realism to characters, enabling them to move smoothly during animation. Compounding the difficulty, weight painting 3D models is a tedious task that can take an expert up to 2 days (or around 16 work hours) to manually complete one model. Smaller animation shops often do not have the expertise to perform this task at all and are unable to compete for bigger, more lucrative contracts. Furthermore, researchers and students at universities around the world are often unable to perform this weighting task, which reduces their ability to create animations for medical, athletic, and entertainment uses in augmented reality or virtual reality.
This Small Business Innovation Research (SBIR) Phase I project will utilize deep neural networks (DNN) to create 3D models from text input as well as a weight-painted rig from an industry-standard skeleton system and a 3D model mesh. The technology converts the mesh and skeleton into a format that can be processed by machine learning (ML) code, introducing a brand-new data structure. Additionally, the project will explore an adaptation of the COO (Coordinate List) matrix, a sparse matrix that performs effectively with neural networks but faces challenges when applied to machine learning tasks in 3D space where coordinate ordering is uncertain. The most difficult issues, such as weight painting and modeling, have been hampered by four specific limitations: 1. Lack of ground-truth, 2. Limited training data, 3. Lack of a-priori ML architecture, and 4. Lack of robustness and specificity for non-gaussian data. This project will make inroads into each of these areas by establishing a methodology for incorporating and transforming non-gaussian data for DNN analysis and will create a comprehensive data training set while establishing a domain specific ground-truth based on the canonical Turing test.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
AV
Solicitation Number
NSF 23-515
Status
(Complete)
Last Modified 12/5/23
Period of Performance
12/1/23
Start Date
11/30/24
End Date
Funding Split
$275.0K
Federal Obligation
$0.0
Non-Federal Obligation
$275.0K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2334192
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
FB8PPCZVCXA8
Awardee CAGE
None
Performance District
GA-10
Senators
Jon Ossoff
Raphael Warnock
Raphael Warnock
Modified: 12/5/23