The accelerating demand for immersive digital experiences, from enterprise training simulations to consumer entertainment, is pushing the boundaries of 3D content creation. Traditional methods are costly and time-consuming, creating a bottleneck for innovation. This technology offers a critical solution by automating and simplifying the 3D modeling process, enabling companies to scale content production, reduce operational expenses by up to 50%, and rapidly deploy new applications in a competitive global market.
Generates High-Precision 3D Models with Simple Configuration: Achieves high-quality human 3D model generation from 2D video with a simpler system configuration compared to conventional methods, by reconstructing and integrating facial textures, 3D shapes, and poses.
Significantly Reduces Development and Operational Costs: Eliminates the need for complex multi-camera systems or specialized manual modeling, potentially reducing 3D model production development time and labor costs by up to 50%.
Ensures Strong Market Advantage and Long-Term Exclusivity: Registered with only two prior art documents, indicating high technical originality. The exclusive period until 2042 provides a strong differentiator against competitors.
This patent protects an information processing system, method, and program for generating 3D human models from 2D video, covering the reconstruction and integration of facial textures, 3D shapes, and poses. Its rapid grant without rejections and limited prior art indicate strong novelty and inventive step, providing a robust and stable intellectual property foundation for licensees.
While strong in 3D human model generation from 2D video, this patent does not explicitly cover advanced animation rigging, real-time physics simulation for digital garments, or integration with haptic feedback systems, offering avenues for licensees to develop complementary IP.
Traditional 3D model generation requires specialized designers and equipment, with an estimated cost of ~$3.5K (AI est.) per model, totaling ~$1M/year (AI est.) for 300 models annually. Implementing this technology could reduce reliance on specialized personnel and automate the generation process, potentially cutting costs by 50%. This would lead to a direct cost reduction of ~$500K/year (AI est.). Considering accelerated market entry and reduced opportunity loss from shorter development cycles, an economic impact exceeding ~$1M/year (AI est.) is projected.
X: 3D Model Generation Efficiency
Y: Real-time Capability & Accuracy