Creating attractive tourism content! Video, images, 3D generation AI.
In the tourism industry, it is important to effectively convey the charm of the region and attract tourists. Engaging images, videos, and 3D content significantly contribute to raising awareness of tourist destinations and attracting visitors. However, there is a challenge in that producing high-quality content requires time and cost. DiFarsion, as a contract research and development brand specializing in image, video, and 3D generation AI, addresses these challenges. 【Usage Scenarios】 - Production of promotional videos for tourist destinations - Tourism experience content utilizing VR/AR - Virtual tours of tourist facilities using 3D models - Image generation for social media campaigns 【Benefits of Implementation】 - Increased attraction power through engaging content - Reduction in production costs and time - Information dissemination through diverse expressions - Differentiation through the latest technology
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basic information
【Features】 - Supports image, video, and 3D generation using diffusion models, NeRF, Gaussian Splatting, and more. - Investigates the latest generative AI models and consistently handles research, implementation, and evaluation according to requirements. - Develops high-precision generation and reconstruction systems by leveraging expertise in image analysis, video analysis, and 3D analysis. - Capable of handling everything from prototypes for research purposes to system development aimed at practical operation. - Supports the design of learning, inference, quality evaluation, and improvement cycles for generative models. 【Our Strengths】 We are engaged in contract development and our own product offerings related to artificial intelligence. Since 2013, we have shifted to Deep Learning and have been working on improvements to CNNs and RNNs. We have a track record of leading the industry since the dawn of GANs and have experience writing for Shuwa System in the early days. Please feel free to contact us with your requests.
Price information
【Price Information】 - Prices will be individually quoted based on the target theme, required generation accuracy, presence of research elements, and scope of development. - We will flexibly respond from PoC and prototype development to full-scale implementation, proposing the optimal configuration according to your requests and budget. - Please feel free to contact us for more details.
Delivery Time
※The delivery time varies depending on the content, but prompt response is possible.
Applications/Examples of results
【Applications and Achievements】 - Development of a 3D generation system utilizing Score Distillation Sampling After researching the latest technologies related to diffusion models and NeRF models, we developed a 3D generation system using Score Distillation Sampling. We validated and implemented a mechanism to generate high-quality 3D representations while leveraging insights from 2D generation models. - Acceleration of training and inference in diffusion-based image generation applications In image generation applications using diffusion models, we implemented processing acceleration during both training and inference. By optimizing the model structure, inference flow, and implementation methods, we worked on improving response speed and usability with an eye toward practical operation. - Fast video generation utilizing Gaussian Splatting We researched and developed a video generation system capable of fast rendering and generation using Gaussian Splatting. By leveraging the strengths of 3D representation, we built a mechanism that operates more lightweight and faster than conventional methods, deploying it as a highly practical generation technology.
Company information
Our company is engaged in various artificial intelligence-related businesses, including artificial intelligence development, artificial intelligence consulting, artificial intelligence seminars, and artificial intelligence application development. Since 2013, we have shifted to Deep Learning, working on improvements to CNNs and RNNs, and by 2015, we developed our own model using RNNs. Additionally, starting in 2016, we expanded our focus to deep reinforcement learning, developing numerous simulation models and robot models. Please feel free to contact us if you have any inquiries.
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