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OpenWorldLib: A Unified Codebase and Definition of Advanced World Models
DataFlow Team, Bohan Zeng, Daili Hua, Kaixin Zhu, Yifan Dai, Bozhou Li, Yuran Wang, Chengzhuo Tong, Yifan Yang, Mingkun Chang, Jianbin Zhao, Zhou Liu, Hao Liang, Xiaochen Ma, Ruichuan An, Junbo Niu, Zimo Meng, Tianyi Bai, Meiyi Qiang, Huanyao Zhang, Zhiyou Xiao, Tianyu Guo, Qinhan Yu, Runhao Zhao, Zhengpin Li, Xinyi Huang, Yisheng Pan, Yiwen Tang, Juanxi Tian, Yang Shi, Yue Ding, Xinlong Chen, Hongcheng Gao, Minglei Shi, Jialong Wu, Zekun Wang, Yuanxing Zhang, Xintao Wang, Pengfei Wan, Yiren Song, Mike Zheng Shou, Wentao Zhang
TL;DR
A clear, unified definition of world models is lacking as AI systems move toward real-world applications. The paper defines world models around perception, interaction, and long-term memory, then introduces OpenWorldLib to standardize capabilities and inference across tasks.
Problem
A clear, unified definition of world models is lacking, while models are increasingly expected to move from virtual environments into real-world applications.
Method
The paper defines world models as perception-centered models or frameworks with action-conditioned simulation and long-term memory, and builds OpenWorldLib to unify inference across diverse tasks.
Results
OpenWorldLib provides a standardized workflow and evaluation pipeline with unified interfaces for interactive video generation, 3D scene reconstruction, multimodal perception, and interaction controls.
Takeaways & Limitations
The framework is intended as a practical reference for integrating world-model tasks and facilitating future research and fair comparisons.
Takeaways & Limitations
3D generation still faces geometric inconsistency and texture blurring during significant camera movement and in complex areas.
Abstract
from arXiv · showhide
World models have garnered significant attention as a promising research direction in artificial intelligence, yet a clear and unified definition remains lacking. In this paper, we introduce OpenWorldLib, a comprehensive and standardized inference framework for Advanced World Models. Drawing on the evolution of world models, we propose a clear definition: a world model is a model or framework centered on perception, equipped with interaction and long-term memory capabilities, for understanding and predicting the complex world. We further systematically categorize the essential capabilities of world models. Based on this definition, OpenWorldLib integrates models across different tasks within a unified framework, enabling efficient reuse and collaborative inference. Finally, we present additional reflections and analyses on potential future directions for world model research. Code link: https://github.com/OpenDCAI/OpenWorldLib
1 Introduction
The paper addresses the lack of consensus around world-model definitions by proposing a capability-centered definition and building OpenWorldLib as a unified inference framework. It also identifies the framework’s research contributions and discusses future development directions.
- World-model definitions and scopes remain diverse despite prior surveys and position papers, with no broadly accepted consensus.
- The paper defines a world model as a perception-centered model or framework with action-conditioned simulation and long-term memory for understanding and predicting complex-world dynamics.
- OpenWorldLib unifies inference for interactive video generation, 3D generation, multimodal reasoning, and vision-language-action tasks.
- The paper clarifies which tasks belong to world-model capabilities and which are often mistakenly treated as world-model objectives.
- The framework is proposed to structure and standardize world-model research, alongside further analysis of its future development.
2 Background and Related Works
World models are framed as systems that perceive, understand, remember, and predict complex environments, while the literature distinguishes core capabilities from tasks that merely resemble them. The paper highlights interactive generation, multimodal reasoning, embodied action, and structured 3D representations, while excluding applications lacking physical-world understanding.
- World model foundations: World models are commonly described through state transitions, observations, actions, rewards, and latent memory for long-horizon dependencies.These formulations alone do not establish that a task serves the core purpose of world modeling.
- Core capabilities: Interactive video generation is a central world-model paradigm, progressing from regression-based next-frame prediction toward diffusion and unified multimodal approaches.The cited developments target generation quality and controllability.
- Core capabilities: Multimodal reasoning covers spatial, omni, temporal, and causal understanding as key reflections of a world model’s physical-world comprehension.The paper also notes latent reasoning as an emerging approach beyond traditional explicit reasoning.
- Core capabilities: Vision-Language-Action is crucial because embodied devices provide a primary means for agents to interact with complex physical environments.Robotic research predicts actions directly or combines action prediction with video generation for planning.
- Core capabilities: 3D representations and reconstruction provide verifiable environments that preserve physical consistency during long-term interactions and support simulator-based testing.Fast scene generation is needed for real-time simulator use.
- Excluded applications: Text-to-video generation, code generation, web search, and entertainment-focused avatar generation are excluded when they lack physical-world understanding or focus on complex-world exploration.Output format, multimodality, or long-term interaction alone is insufficient under the paper’s definition.
3 OpenWorldLib Framework Design
OpenWorldLib translates the paper’s capability requirements into a unified framework that standardizes multimodal inputs and routes them through reasoning, synthesis, representation, and action-oriented modules. Its templates and integration surfaces support consistent invocation of heterogeneous pretrained and hosted models.
- Framework overview: OpenWorldLib addresses the need for physical-world inputs, understanding, long-term memory, and multimodal outputs through a concrete unified implementation.The framework is presented as an engineering standard for invoking diverse world-model capabilities.
- Operator: The Operator bridges raw user or environmental signals and execution modules by validating inputs and preprocessing them into standardized representations.Supported signals include text, images, control actions, and audio.
- Operator: A unified Operator template gives task-specific operators a consistent API across the codebase.The template includes interaction retrieval, checking, processing, and perception hooks.
- Synthesis: The Synthesis module converts standardized conditioning into visual, auditory, and embodied outputs through heterogeneous generative backends.It returns structured artifacts and metadata for export, evaluation, or memory.
- Synthesis: Visual synthesis supports image and video generation from prompts, reference images, or scene specifications, with controls for resolution, frame budget, and guidance.It supports both checkpoint-driven local pipelines and hosted-service wrappers.
- Synthesis: Audio synthesis produces conditioned waveforms, while VLA synthesis maps multimodal context into executable action sequences for closed-loop interaction.VLA interfaces align diverse action representations with simulators or robotic hardware.
3.3 Reasoning Module
The Reasoning module equips world models with structured understanding before generation or action. It unifies general, spatial, and audio reasoning methods behind a shared BaseReasoning interface.
- Reasoning capabilities: The Reasoning module supports spatial inference, multimodal context integration, and grounded semantic interpretation before downstream generation or action.Its design reflects the need to understand the physical world beyond perception alone.
- Reasoning capabilities: General reasoning uses multimodal models that process text, images, audio, and video in a unified manner.This is one of three reasoning sub-categories described by the framework.
- Reasoning capabilities: Spatial reasoning targets 3D understanding and object localization from visual observations, while audio reasoning interprets auditory signals.These capabilities specialize the framework’s physical-world inference.
- Framework interface: BaseReasoning provides a unified template whose task-specific classes share a consistent API across the codebase.The template supports pretrained initialization and inference-ready reasoning instances.
3.4 Representation Module
The Representation module converts perceptual inputs into explicit structured representations and supports their use in simulation and external systems. OpenWorldLib separates representation from synthesis and standardizes both representation and memory interfaces.
- Representation Module: The Representation module bridges raw perception and structured simulation by handling explicit representations such as 3D structures.It is separated from Synthesis because structured representations differ from perception data directly collected from the world.
- Representation Module: It transforms input data into explicit 3D outputs, including point clouds, depth maps, camera poses, and masks.
- Representation Module: The module creates manual environments where predicted actions can be tested and validated in a coordinate system.
- Representation Module: OpenWorldLib supports local inference and cloud-based APIs for exporting explicit representations to external physics engines.
- Representation Module: The unified BaseRepresentation template gives task-specific representation classes a consistent API.It standardizes model initialization, device placement, and data-type configuration.
- Memory Module: The unified Memory module stores multimodal interaction history, retrieves relevant context, updates state, and manages task- or session-specific memory.It supports text, visual features, action trajectories, and scene states across interactions.
3.6 Pipeline
OpenWorldLib uses a unified Pipeline as the top-level entry point for coordinating world-model modules. It supports standardized single-turn inference, stateful multi-turn interaction, modular execution, and structured outputs.
- Pipeline: The Pipeline integrates Operator, Reasoning, Synthesis, Representation, and Memory modules into one cohesive execution system.It encapsulates initialization, data flow, module invocation, memory interaction, and post-processing.
- Pipeline: The standard workflow validates and preprocesses inputs, retrieves historical context, coordinates core modules, returns structured outputs, and updates memory.The design decouples module implementations while maintaining data transmission across the pipeline.
- Pipeline: A single from_pretrained() interface loads weights, configures devices, and instantiates the pipeline's submodules.
- Pipeline: The __call__() method provides one-click forward inference for single-turn world-model tasks.
- Pipeline: The stream() method supports stateful multi-turn interaction with persistent memory reading and writing.
- Pipeline: The Pipeline dynamically invokes Reasoning, Synthesis, or Representation according to task type and formats results for downstream use.Outputs can support visualization, evaluation, logging, or downstream control systems.
4 Discussion
The discussion argues that world models must move beyond next-frame prediction alone toward efficient, broad interaction with complex physical environments. It also highlights data-centric improvements and hardware or architecture changes as future needs.
- Discussion: Current architectures often focus on next-frame prediction, but the paper argues that necessary world-model functions should be implemented before emphasizing specific structures.
- Discussion: Multimodal data synthesis, domain-specific augmentation, dynamic training, and training-data quality evaluation are identified as important data-centric directions.
- Discussion: Next-frame prediction retains more information than next-token prediction during real-world interaction, but its efficiency needs significant improvement.
5 Evaluation
OpenWorldLib evaluates world-model tasks spanning video generation, multimodal reasoning, 3D generation, and simulation-based embodied interaction. The reported comparisons show method-specific strengths alongside persistent realism, consistency, and efficiency challenges.
- Evaluation: The evaluation uses NVIDIA A800 and H200 GPUs and includes interactive video, multimodal reasoning, 3D generation, and simulator-based tasks.
- Interactive Video Generation: Hunyuan-WorldPlay achieves the best overall visual performance among the compared navigation video generation methods.
- Interactive Video Generation: Wan-IT2V supports basic interactive video generation but struggles to maintain physical consistency.
- Interactive Video Generation: Cosmos provides higher generation quality and physical realism than WoW for complex interactive operations.
- 3D Generation: VGGT and InfiniteVGGT exhibit geometric inconsistency and texture blurring under significant camera movement, while FlashWorld faces a speed-detail trade-off.
- Simulation: AI2-THOR and LIBERO provide complementary simulation settings for embodied video generation and Vision-Language-Action evaluation.The framework presents manipulation tasks, embodied interaction scenarios, and evaluations across VLA methods.
6 Conclusion
OpenWorldLib presents a standardized workflow and evaluation pipeline for world models, with unified interfaces for interactive video generation and 3D scene reconstruction. It standardizes multimodal perceptual inputs and diverse interaction controls to support future research and fair comparisons.
- OpenWorldLib presents a standardized workflow and evaluation pipeline for world models.It provides unified interfaces for interactive video generation and 3D scene reconstruction.
- The framework standardizes integration of multimodal perceptual inputs and diverse interaction controls.
- OpenWorldLib is intended as a practical reference for future world model research and fair comparisons.
A Author Contributions
The author contributions span project supervision, framework design, core pipeline development, technical guidance, testing, promotion, and manuscript preparation. Contributors cover interactive video, 3D generation, VLA, reasoning, audio, simulation, and related world model tasks.
- Core contributors developed pipelines for interactive video generation, 3D generation, VLA, reasoning, simulation, and audio-related tasks.The contributions identify task-specific pipeline development across these areas.
- Other contributors provided technical or knowledge guidance on world model reasoning, synthesis, memory, VLA, and unified reasoning and generation.
- Several contributors tested OpenWorldLib pipelines and promoted the framework.
- Project supervision and framework design were led by designated supervisors and core contributors.The listed roles include project supervision, framework design, manuscript writing, and scope definition for world model tasks.