Cloud-based collaborative animation generation with distributed rendering and resource scheduling

Animation production is increasingly shaped by collaborative workflows and compute-intensive pipelines, where content creation, model inference, and high-quality rendering are often conducted in parallel by geographically distributed users. However, conventional animation tools are largely built around standalone environments, making it difficult to scale computation resources or support efficient multi-user collaboration. Cloud platforms offer elastic resources and distributed execution capabilities, yet integrating animation generation, rendering, and collaboration into a unified cloud-based system remains challenging. This paper presents a cloud-based collaborative animation generation system that integrates open-source generative models with distributed rendering and resource scheduling services. The proposed framework enables multiple users to concurrently submit animation tasks, which are decomposed into generation and rendering subtasks and executed across cloud nodes through a coordinated orchestration layer. A distributed rendering pipeline is developed to support scalable execution of Blender-based workloads, while a resource scheduling mechanism is designed to manage heterogeneous GPU resources and dynamically allocate tasks according to workload conditions. Extensive experiments are conducted to evaluate both system-level and animation-oriented performance. The results demonstrate that the proposed system can effectively support concurrent animation creation while achieving stable performance gains and improved resource efficiency in cloud environments.

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Publication Details

Journal
Journal of Cloud Computing Advances Systems and Applications
Published
2026-03-07
DOI
https://doi.org/10.1186/s13677-026-00876-8
Primary Topic
Human Motion and Animation
Type
article
Field-Weighted Citation Impact
0.00
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article

Cloud-based collaborative animation generation with distributed rendering and resource scheduling

Kaiwen Qiu, Yiqun Tan, Wei-Jia Huang, Jing Zhu
Journal of Cloud Computing Advances Systems and Applications
Human Motion and Animation
article

Cloud-based collaborative animation generation with distributed rendering and resource scheduling

Kaiwen Qiu, Yiqun Tan, Wei-Jia Huang, Jing Zhu
article en

Abstract

Animation production is increasingly shaped by collaborative workflows and compute-intensive pipelines, where content creation, model inference, and high-quality rendering are often conducted in parallel by geographically distributed users. However, conventional animation tools are largely built around standalone environments, making it difficult to scale computation resources or support efficient multi-user collaboration. Cloud platforms offer elastic resources and distributed execution capabilities, yet integrating animation generation, rendering, and collaboration into a unified cloud-based system remains challenging. This paper presents a cloud-based collaborative animation generation system that integrates open-source generative models with distributed rendering and resource scheduling services. The proposed framework enables multiple users to concurrently submit animation tasks, which are decomposed into generation and rendering subtasks and executed across cloud nodes through a coordinated orchestration layer. A distributed rendering pipeline is developed to support scalable execution of Blender-based workloads, while a resource scheduling mechanism is designed to manage heterogeneous GPU resources and dynamically allocate tasks according to workload conditions. Extensive experiments are conducted to evaluate both system-level and animation-oriented performance. The results demonstrate that the proposed system can effectively support concurrent animation creation while achieving stable performance gains and improved resource efficiency in cloud environments.

Journal of Cloud Computing Advances Systems and Applications
Anhui University (CN), Jiangxi Applied Engineering Vocational Institute (CN), Nanning Normal University (CN), Northern University of Malaysia (MY)
Openalex Percentile: Top 33%
Human Motion and Animation
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Cloud-based collaborative animation generation with distributed rendering and resource scheduling — Kaiwen Qiu, Yiqun Tan, et al. · Journal of Cloud Computing Advances Systems and Applications (2026) | TGRS Research Map | TGRS