Photo: Sri Lanka Gardien
SHENZHEN, 11 March (BelTA - Xinhua) - Chinese scientists have developed a
high-precision three-dimensional (3D) face database and achieved a
breakthrough in personalized modeling, which will strongly support more
natural human-computer interaction.
To enable virtual humans to express vivid emotions, recognize human identities and demonstrate embodied intelligence, the key technology is 3D facial keypoint detection.
However, the lack of large-scale and precisely annotated 3D facial datasets means that most current 3D facial landmark detection algorithms rely on 2D texture assistance or non-photorealistic digital 3D faces. Such 3D facial keypoint detection has long been hindered by insufficient data and poor generalization ability.
A research team from the Shenzhen Institutes of Advanced Technology of the Chinese Academy of Sciences and Fujian University of Technology has developed a new curvature-fused graph attention network (CF-GAT) capable of predicting facial landmarks directly from raw point clouds, which helps achieve an essential improvement from "one-size-fits-all" to personalized modeling.
This study was published last week in the journal IEEE Transactions on Circuits and Systems for Video Technology.
The research team built a custom 3D/4D facial acquisition system and conducted standardized data collection, creating what it said is the industry's largest high-precision, accurately annotated 3D facial database to date, comprising approximately 200,000 high-fidelity 3D facial scans.
On this basis, the database system also includes a multi-expression 3D face dataset, a standardized 3D facial landmark dataset, a high-precision 3D human body dataset, and a dynamic 4D facial expression dataset.
"These databases have become core support in the key technology chain of humanoid robots, providing basic data for high-fidelity perception, expression modeling and behavior generation," the corresponding author Song Zhan said.
"In the future, these datasets will further serve the data-driven large-model humanoid robot system to build more natural and intelligent human-robot interaction capabilities," he added.
To enable virtual humans to express vivid emotions, recognize human identities and demonstrate embodied intelligence, the key technology is 3D facial keypoint detection.
However, the lack of large-scale and precisely annotated 3D facial datasets means that most current 3D facial landmark detection algorithms rely on 2D texture assistance or non-photorealistic digital 3D faces. Such 3D facial keypoint detection has long been hindered by insufficient data and poor generalization ability.
A research team from the Shenzhen Institutes of Advanced Technology of the Chinese Academy of Sciences and Fujian University of Technology has developed a new curvature-fused graph attention network (CF-GAT) capable of predicting facial landmarks directly from raw point clouds, which helps achieve an essential improvement from "one-size-fits-all" to personalized modeling.
This study was published last week in the journal IEEE Transactions on Circuits and Systems for Video Technology.
The research team built a custom 3D/4D facial acquisition system and conducted standardized data collection, creating what it said is the industry's largest high-precision, accurately annotated 3D facial database to date, comprising approximately 200,000 high-fidelity 3D facial scans.
On this basis, the database system also includes a multi-expression 3D face dataset, a standardized 3D facial landmark dataset, a high-precision 3D human body dataset, and a dynamic 4D facial expression dataset.
"These databases have become core support in the key technology chain of humanoid robots, providing basic data for high-fidelity perception, expression modeling and behavior generation," the corresponding author Song Zhan said.
"In the future, these datasets will further serve the data-driven large-model humanoid robot system to build more natural and intelligent human-robot interaction capabilities," he added.
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