MicroVerse: A Preliminary Exploration Toward a Micro-World Simulation
Abstract
Current video generation models struggle with microscale simulation tasks, prompting the development of MicroVerse, a specialized video generation model trained on expert-verified simulation data to accurately reproduce complex microscopic mechanisms.
Recent advances in video generation have opened new avenues for macroscopic simulation of complex dynamic systems, but their application to microscopic phenomena remains largely unexplored. Microscale simulation holds great promise for biomedical applications such as drug discovery, organ-on-chip systems, and disease mechanism studies, while also showing potential in education and interactive visualization. In this work, we introduce MicroWorldBench, a multi-level rubric-based benchmark for microscale simulation tasks. MicroWorldBench enables systematic, rubric-based evaluation through 459 unique expert-annotated criteria spanning multiple microscale simulation task (e.g., organ-level processes, cellular dynamics, and subcellular molecular interactions) and evaluation dimensions (e.g., scientific fidelity, visual quality, instruction following). MicroWorldBench reveals that current SOTA video generation models fail in microscale simulation, showing violations of physical laws, temporal inconsistency, and misalignment with expert criteria. To address these limitations, we construct MicroSim-10K, a high-quality, expert-verified simulation dataset. Leveraging this dataset, we train MicroVerse, a video generation model tailored for microscale simulation. MicroVerse can accurately reproduce complex microscale mechanism. Our work first introduce the concept of Micro-World Simulation and present a proof of concept, paving the way for applications in biology, education, and scientific visualization. Our work demonstrates the potential of educational microscale simulations of biological mechanisms. Our data and code are publicly available at https://github.com/FreedomIntelligence/MicroVerse
Community
In the current era of rapid advancements in video generation, while macroscopic simulation has reached unprecedented heights, the microscopic realm — the fundamental engine of life and science — remained a largely uncharted "black box" for AI.
MicroVerse marks a seminal exploration into the concept of Micro-World Simulation. We have identified a critical "fidelity gap" in existing state-of-the-art models like Sora and Veo: while they can generate visually stunning videos, they fundamentally fail to adhere to the rigid physical and biological laws of the micro-scale. MicroVerse is our answer to this challenge, redefining the boundary between generative AI and scientific reality.
🔬 Core Pillars of Our Work
- MicroWorldBench: The industry’s first multi-level, rubric-based benchmark. Featuring 459 unique expert-annotated criteria, it spans organ-level processes, cellular dynamics, and subcellular molecular interactions, providing the first rigorous "scientific yardstick" for micro-intelligence.
- MicroSim-10K: A world-first, expert-verified dataset containing 9,601 high-quality micro-simulation scenarios. This serves as the "physical grounding" that current models lack, prioritizing biological accuracy over mere pixels.
- The MicroVerse Model: A tailored video generation framework that explicitly incorporates physical grounding and fine-grained biological dynamics. MicroVerse doesn't just "paint" a picture; it reproduces complex microscale mechanisms with a +2.7 gain in scientific fidelity over leading baselines.
🌟 Our Vision
By bridging the gap between simulated visual data and real experimental distributions, we are paving the way for a new generation of "World Models" capable of driving breakthroughs in drug discovery, disease modeling, and interactive scientific visualization. > MicroVerse is not just a model; it is the foundation for understanding and simulating the fundamental mechanisms of life.
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