凌迪科技亮相 WAIC 2026：自研仿真引擎从服装延伸到具身智能训练

This article introduces the latest achievements of the self-developed simulation engine exhibited by Style3D (Lingdi Technology) at the 2026 World Artificial Intelligence Conference, with key takeaways as follows:

1. Lingdi Technology has long focused on the field of physics-based AI, with its core technology lying in a self-developed simulation engine that endows digital objects with shapes, motions and interaction rules close to those in the real world. The technology was first implemented in 3D simulation for the textile and apparel industry, later expanded to the game and film sectors, and has now extended to the embodied intelligent training space, while its underlying logic has remained consistent throughout this process.

2. The company showcased three core products at the conference: SynReal World, a full-stack data engine for embodied intelligence that enables a closed loop from robot virtual training to real-world deployment, addressing the high cost and scarcity of real training samples; StyleWork, an AI middle platform for the light apparel industry that supports collaboration across all business processes; and Stylebolt, an in-store marketing robot for the fashion industry.

3. Lingdi Technology has currently established partnerships with multiple AI and robotics institutions to jointly explore commercial applications of the technology.

This article provides multi-dimensional references for technology and fashion brands in terms of R&D, brand marketing and scenario deployment, with key insights as follows:

1. R&D path: The approach of expanding application scenarios horizontally based on a core technology foundation is proven feasible. Starting from flexible garment simulation, Lingdi has gradually extended to the CG and embodied intelligence sectors while keeping its underlying logic unchanged, which effectively reduces trial-and-error costs for new technology R&D. This model is a valuable reference for technology-driven brands.

2. Product deployment direction: AI has now penetrated the entire apparel industry value chain and offline retail. StyleWork enables collaboration across design, product selection and marketing links, while in-store robots empower offline retail marketing, aligned with the "AI+ industry" consumer trend. Brands can layout new products around these directions.

3. Brand marketing and ecosystem building: Showcasing new products at top-tier industry events is an efficient approach for brand exposure. Meanwhile, establishing ecosystem partnerships with leading industry players allows companies to rapidly expand technology deployment scenarios and boost brand influence.

This article provides references for new market opportunities and directions for sellers in apparel retail and AI-related tracks, with key takeaways as follows:

1. Market opportunities: The combination of AI and the apparel industry has given rise to multiple deployable new scenarios, including AI collaborative middle platforms for design, product selection and operation, and smart in-store marketing robots. Sellers can layout these related businesses to help the industry cut costs, improve efficiency and upgrade consumer experiences.

2. Shifting demand: Demand for smart marketing tools in offline retail continues to grow, AI upgrades are underway across all links of the apparel industry, and consumer demand for intelligent, personalized services is also rising. Sellers can adjust their business direction around these new demands.

3. Cooperation opportunities: Leading technology companies are currently building open ecosystems around embodied intelligence and flexible simulation. Sellers can connect to these technology platforms to obtain technical support, reduce their own R&D costs, and layout new tracks early to seize first-mover advantages.

This article provides clear direction references for light industrial factories such as apparel factories promoting digital and AI upgrades, with key insights as follows:

1. Product design and production adaptation: AI can now participate in front-end production links including design and product selection. AI middle platforms like StyleWork enable collaboration across all business processes. Introducing such tools can help factories improve design and product selection efficiency, shorten new product development cycles, and better adapt to new production requirements such as fast fashion.

2. Business opportunities and transformation: Mature upstream simulation technology can now support digital transformation of the apparel industry, and the threshold for factories to access mature AI tools continues to fall. Factories can seize the current technology window to advance their own digital transformation and improve market competitiveness.

3. Cost reduction and efficiency improvement insights: Core simulation technology solves the problem of digitizing flexible objects, enabling high-precision 3D simulation of fabrics and garments, and helping factories cut production costs in links such as physical sampling. Factories can focus on the deployment and application of this type of technology.

This article shares insights on industry development trends, customer pain points and reference solutions for AI technology service providers and industrial digital service providers, with key takeaways as follows:

1. Industry development trend: Physical simulation + AI is currently a key development direction in the AI sector. Underlying simulation technology can extend from traditional industrial scenarios to emerging fields such as embodied intelligence, with huge market space for cross-scenario expansion. Technology service providers can make long-term layouts centered on core underlying technologies.

2. Customer pain point analysis: In the embodied intelligence R&D space, high costs for real-world scenario data collection and limited sample coverage are core pain points widely faced across the industry; meanwhile, demand for AI collaboration across the entire process of the light apparel industry has not been fully met, leaving market gaps.

3. Replicable solutions: To address the pain points of embodied intelligence training, companies can build a full-stack data engine covering data generation, virtual training, simulation evaluation and real-world deployment. Virtual simulation can be used to supplement scarce samples, forming a closed training loop from digital space to the real world to effectively solve customer pain points.

This article provides clear insights on industry demand, ecosystem building directions and risk warnings for artificial intelligence and industrial service platforms, with key takeaways as follows:

1. Investment and onboarding direction: There is currently strong market demand for mature self-developed simulation engines and full-stack data training platforms in the industrial AI and embodied intelligence sectors, and relevant technology companies are growing rapidly. Platforms can strategically onboard these high-quality enterprises with core independent technologies to improve their ecosystem layout.

2. Operation and management direction: The development of simulation technology and embodied intelligence requires cross-domain collaboration covering technology R&D, industrial deployment, robotics development and other links. Platforms can build special cooperation docking platforms centered on flexible simulation and embodied intelligent training to facilitate collaboration between different stakeholders and accelerate technology deployment.

3. Risk mitigation: Independent research and development of core underlying technologies is a clear industry trend, and enterprises with independent core technology foundations have stronger competitiveness. Platforms can prioritize supporting these enterprises to avoid potential risks brought by relying on external technology.

This article provides references for the latest industry developments and research directions for researchers in artificial intelligence and industrial digitalization, with key takeaways as follows:

1. New industry development: Cross-scenario reuse and extension of core technology has become a new development path for technology-driven enterprises. Lingdi Technology's self-developed simulation engine has gradually expanded from flexible object simulation for textiles and apparel to CG content production and embodied intelligent robot training, verifying the feasibility of cross-scenario deployment of underlying core technologies and providing a new paradigm for industrial development.

2. New research directions: The industry-wide pain point of high real data collection costs and insufficient samples in embodied intelligent training has been partially addressed through the approach of a full-stack virtual simulation engine. The model that connects the full closed loop of data generation, training, evaluation and deployment has high research value.

3. Business model research direction: The "core technology foundation + multi-scenario deployment" model can amortize core technology R&D costs and gradually expand business boundaries, making it a new business model worthy of research for technology-driven enterprises. The current ecosystem-driven deployment model also carries research significance.