Infinite Scene Generation for Visual Spatial Reasoning: A Customizable AI Benchmarking Platform
InfiniBench is an automated benchmark generation system that synthesizes an effectively unlimited variety of photorealistic 3D scenes with fully parameterized control over scene complexity. By enabling precise, language-driven customization of 3D environments, it provides an unprecedented tool for evaluating and training AI systems on visual spatial reasoning tasks.
Description
InfiniBench addresses a critical gap in AI evaluation by generating diverse, controllable 3D scene benchmarks from simple natural language prompts. The system translates scene descriptions into physically plausible, photorealistic video sequences through three integrated innovations: an LLM-based agentic framework that iteratively refines procedural scene constraints; a cluster-based layout optimizer capable of producing dense, cluttered environments that were previously intractable for standard procedural methods; and a task-aware camera trajectory optimization module that ensures full object coverage throughout the rendered video. The result is a scalable, user-friendly platform that offers granular control over the number, type, and spatial arrangement of objects in a scene. Unlike static or narrowly scoped benchmarks, InfiniBench can generate an essentially infinite range of complexity levels, enabling researchers and developers to isolate specific AI failure modes, stress-test models under precisely defined spatial conditions, and produce high-quality synthetic training data on demand.Applications
- Evaluation and benchmarking platforms for vision-language model (VLM) developers- Synthetic training data generation for robotics and embodied AI foundational models
- Pre-deployment simulation and validation environments for robotic systems
- AI testing infrastructure for autonomous vehicles and navigation systems
- Research tooling for academic and industry labs studying spatial cognition in AI
Advantages
- Fully controllable scene complexity via natural language input, requiring no specialized programming knowledge- Capable of generating theoretically unlimited, non-repetitive benchmark scenarios to prevent dataset saturation
- Outperforms leading procedural and LLM-based 3D generation methods in prompt fidelity and physical plausibility, particularly in high-complexity settings
- Enables targeted diagnosis of AI failure modes by isolating specific spatial reasoning conditions
- Applicable across both benchmarking and synthetic training data generation for embodied AI and robotics
Invention Readiness
The technology has advanced beyond initial concept to a fully functional prototype with validated performance. A working software implementation exists and has been tested, demonstrating superior results compared to state-of-the-art alternatives across multiple spatial reasoning tasks including measurement, perspective-taking, and spatiotemporal tracking. Further development efforts may focus on expanding the range of supported scene types, broadening integration with downstream AI training pipelines, and conducting additional validation studies across a wider variety of robotic and embodied AI platforms.IP Status
CopyrightRelated Publication(s)
Wang, Haoming, Qiyao Xue, and Wei Gao. "InfiniBench: Infinite Benchmarking for Visual Spatial Reasoning with Customizable Scene Complexity." arXiv preprint arXiv:2511.18200 (2025).