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"Generative Reality AI" pioneers a paradigm shift by harnessing the potential of Time-of-Flight sensors, creating a dynamic multimodal model. The fusion of precise sensor data with robust AI capabilities establishes a new standard for real-world perception. Diverging from traditional Generative AI, it goes a step further by deeply intertwining with sensors, elevating AR/VR interactions, and significantly improving efficiency across various applications. Compatible with platforms like OpenAI and LLaMa2, it introduces personalized "Agents" tailored for 3D applications, showcasing adaptability in e-commerce and virtual gaming. Positioned at the forefront of technological progress, Generative Reality AI anticipates impactful roles in autonomous driving, robotics, education, and entertainment, signifying a pivotal moment in the integration of AI.
"Generative Reality AI" gains a competitive edge through its innovative integration of Time-of-Flight sensors and advanced AI models. Seamlessly merging precise sensor data, the technology achieves breakthroughs in imaging and demonstrates practical applications. Beyond content creation, its strengths manifest in heightened user experiences, particularly in healthcare diagnostics and e-commerce. With compatibility across OpenAI and LLaMa2 platforms, it introduces personalized "Agents" tailored for diverse applications. Striking optimizations, including substantial model size reduction and notable processing speed enhancements, underscore its remarkable efficiency. The core innovation in imaging and diffusion semantics unlocks unparalleled practicality in 3D scene understanding and generation. As it reshapes industries, Generative Reality AI emerges as a transformative force, enriching daily life with unparalleled versatility and innovation.
AI Application Scenario Innovation Award.
Outstanding startup award.
Outstanding technology award .
Integrates advanced sensors with a multimodal large model, achieving breakthroughs in imaging and semantic understanding. Optimized for diverse platforms, 8x Cross-Attention quantization improvement, and versatile 3D Agents for applications like e-commerce, AR/VR, medical diagnosis, and robotics.
Precision Sensing
Sensor Collaboration
Data Clarity
Optimal Speed
Universal Applicability
Edge Deployment
Generative Reality AI's Sensor Class revolutionizes sensing with precise Time-of-Flight technology. It seamlessly integrates multimodal data, filters noise, and ensures terminal compatibility. The efficient fusion of sensor tech and AI unlocks universal applicability, making it a milestone in environmental perception for applications like autonomous driving and robotics.
The Model Class of Generative Reality AI achieves groundbreaking results through Core Model Fusion, Parameter Optimization, and Cross-Attention Quantization. These innovations significantly reduce model size, boost processing speed, and enhance NPU efficiency. The proactive integration of future tech applications ensures a versatile and efficient AI model for various domains..
Semantic Integrationg
Size Reduction
Speed Boost
Low Power
Task Specialization
Rich Interaction
Immersive Innovation
Diagnostic Improvement
Generative Reality AI's 3D Agents Class offers diverse and adaptive solutions for 3D applications. These Agents, tailored for tasks like e-commerce, AR/VR, and medical diagnostics, provide personalized, multimodal interactions. Their wide applicability across industries showcases the innovation and potential of Generative Reality AI in shaping immersive and interactive experiences..
The integration of 3D semantics further enriches the understanding and interpretation of captured scenes. Deep AI Features: Combining the physical characteristics of photons with the mechanism of imaging, it obtains high-quality imaging data. Its imaging performance is characterized by improved accuracy and extended operating distance.Intelligent Power: the energy consumption of the Qualcomm Snapdragon DSP to complete the same 1000 inference tasks is only 1/98 of that of the CPU
The "Diffusion IP core" is pivotal in Generative Reality AI, automating scene labeling to enhance data quality and boost lightweight model efficiency. Key features include automatic scene labeling, efficiency improvement for lightweight models, high-speed processing, real-time neural network rendering, and optimization for mobile experiences. This core component is essential for automating scene understanding, improving model efficiency, and achieving high-speed processing, with a focus on real-time hardware acceleration for neural network rendering, particularly on mobile platforms. In summary, the Diffusion IP core significantly contributes to the system's effectiveness, ensuring robust scene labeling and optimal performance in diverse applications.
Instantly records scenes, transforms into images.
Fuse sensors for accurate image data.
Engage through buttons, touch, voice, and gestures.
Capture scenes with immersive depth effects.
Command camera functions with language.
Craft vivid images from textual descriptions.
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Elevate shopping experiences with immersive 3D visuals
Showcase products dynamically for a detailed exploration.
Increase customer interaction with enriched visual content.
Augment reality with realistic and interactive elements.
Enhance user satisfaction through advanced AR/VR features.
Achieve superior scene understanding for a seamless experience.
Achieve accurate analysis of spatial elements.
Optimize space usage with intelligent insights.
Gain a deeper understanding of property features.
Improve diagnostic accuracy with advanced technology.
Utilize cutting-edge imaging techniques for medical assessments.
Enhance insights into medical conditions for better decision-making.
Enable robots to adaptively navigate various environments.
Equip robots with the ability to sense and respond to surroundings.
Enhance robotics decision-making capabilities through environmental awareness.
Clean data is pivotal for LLM and Diffusion models, ensuring accuracy and reliability, enhancing their performance and precision in various applications.
Enhance signal clarity for improved data accuracy.
Maintain scene integrity through effective data restoration methods.
Ensure data consistency through precise calibration and correction processes.
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