3d asset pipeline development

This study addresses the challenge faced by production system engineers in automatically verifying production line layouts due to limited knowledge of PDDL and planning theory. To bridge this gap, the authors propose a novel approach based on an Asset Administration Shell (AAS) capability model that natively generates complete PDDL planning problems directly from domain-level descriptions, eliminating the need for PDDL-specific submodels. The method integrates four Industry 4.0 standards—VDI 3682, IEC 61360-1, IDTA 02011, and IDTA 02016—to construct the AAS and employs an extraction algorithm to automatically translate multi-AAS architectures into PDDL domains. In a laboratory case study, the approach enabled engineers to systematically compare four layout variants by modifying only the AAS model, significantly lowering the barrier to adopting automated planning in industrial settings.

This work addresses the unreliability of developer productivity dashboards, which often stems from ad hoc scripts that introduce undetected silent data gaps, eroding organizational trust. To resolve this, we propose a robust ELT pipeline grounded in DAG-based orchestration and the Medallion architecture, decoupling data extraction from transformation to preserve the immutability of raw data. Our approach introduces a state-driven dependency scheduling mechanism and, for the first time, treats metric pipelines as production-grade distributed systems. We emphasize the critical role of immutable raw history in enabling reliable metric redefinition. This methodology significantly enhances data reliability and freshness while effectively eliminating silent failures, thereby restoring organizational confidence in DevOps metrics.

Existing 3D generation methods struggle to meet production-grade requirements for real-time interactive applications, such as consistent topology, UV unwrapping, physically based rendering (PBR) materials, skeletal rigging, and physically plausible scene layout. To address this gap, this work proposes a two-dimensional taxonomy centered on asset production pipelines—structured by asset type and production stage—and systematically constructs a comprehensive generation framework encompassing geometry synthesis, topology optimization, UV parameterization, PBR appearance modeling, skeletal rigging, and physics-aware scene assembly. The authors further introduce a cross-dimensional evaluation protocol to rigorously assess the direct usability of generated assets in game engines and simulation platforms. Their analysis highlights critical challenges in data quality, controllable generation, and end-to-end assetization, underscoring the pivotal role of deployable 3D content as foundational infrastructure for embodied intelligence and interactive world models.

Existing 3D generation methods struggle to simultaneously achieve high visual fidelity, real-time performance, and mobile deployment. This work proposes the first single-image 3D generation framework that balances deployment efficiency and interactive speed, producing high-quality meshes with baked normals, colored textures, and controllable face counts within 30 seconds; its Flash variant delivers preview-quality results in just 14 seconds. The approach integrates coarse-to-fine VecSet-based geometry generation, multi-view texture synthesis, and 3D back-projection inpainting, while performing mesh simplification, cleanup, normal baking, and parallel UV unwrapping directly on the GPU. Combined with model distillation and pipeline parallelism, the system minimizes end-to-end latency. Experiments demonstrate that the generated assets match the visual quality of commercial solutions, with both automated metrics and blind human evaluations confirming the method’s efficiency and practicality.

This study addresses the challenges of high latency, unstable concurrency, and security risks faced by large language model (LLM) agents in automating asset lifecycle management within Industry 4.0. The authors propose a Plan-then-Execute architecture that generates verifiable workflow graphs and integrates a topology-aware parallel scheduling mechanism to enable controlled inference overlap while ensuring functional correctness and security. Key technical contributions include topological-sort-based multi-agent scheduling, structured context pruning, dependency-aware concurrency control, and graceful degradation under fault injection. Evaluated on the AssetOpsBench benchmark, the system reduces median end-to-end latency by 1.6× (up to 1.8× for highly parallel tasks) and cuts inference overhead by approximately 30% through context pruning, all while maintaining stable task completion rates and output quality.

This work addresses the challenge of tracking multi-hop dependencies and license risks in complex AI model supply chains, where traditional compliance tools fall short. The authors propose the first interactive visual analytics system that integrates 3D spatial layout with path-aware provenance tracing. By leveraging graph layout algorithms, a rule-driven compliance engine, and a multi-scale exploration mechanism, the system enables fine-grained auditing—from global community detection to local path tracing. Empirical evaluation on 908,449 Hugging Face models reveals that 55.46% exhibit compliance risks, including a 56.67% license omission rate in adapter-derived models and an 8.05% license drift rate in fine-tuned models. These findings demonstrate the system’s effectiveness in identifying and attributing license conflicts, omissions, and drifts at scale.