AIAny
Icon for item

ExeWorldBench

Evaluates whether models can reconstruct executable 3D worlds from visual or textual inputs by providing Blender-based tasks, assets, and automated evaluation for code-driven realizations.

Introduction

Why this matters

As models move beyond visual replication to building systems that actually work, benchmarks must measure not just appearance but executable behavior. ExeWorldBench fills this gap by framing world modeling as a code-to-execution problem: the goal is to infer structure, constraints, and causal interactions from visuals or intent and realize them as runnable Blender scenes whose physical behavior can be evaluated automatically.

What Sets It Apart
  • Task design that emphasizes executability: three tiers (Mechanism Executability, Compositional Executability, Intent-Driven Synthesis) progressively test single mechanisms, multi-stage causal chains, and goal-driven world construction — all judged by whether the generated Blender code produces the expected physical behavior rather than pixel similarity.
  • End-to-end evaluation assets: each task includes reference images/frames, agent prompts for API-driven workflows, harness files for offline execution, and judge prompts/scripts to score Visual Structure and Mechanical Motion, enabling reproducible, automated benchmarking of visual-to-code and text-to-code agents.
  • Focus on reasoning + realization: instead of penalizing superficial visual differences, the suite rewards correct component relationships, constraints, and motion dynamics, making it specifically useful for assessing embodied, physically grounded code-generation capabilities.
Suitable For & Tradeoffs

Great fit if you want to benchmark or develop systems that must infer object relationships, constraints, and dynamic behavior and then realize them as runnable 3D code (e.g., coding agents, multimodal VLMs that emit Blender scripts, research on physical reasoning). The dataset includes prompts and evaluation harnesses to streamline comparisons.

Look elsewhere if your goal is pure image generation or visual fidelity benchmarks: this suite prioritizes mechanical correctness and executable outcome over photorealistic rendering. It also assumes familiarity with Blender-based execution and a willingness to run the provided harnesses to obtain final scores.

Information

  • Websitehuggingface.co
  • AuthorsXinge Peng, Zhen Fang, Ruibing Wang, Yongxuan Lv, Haodong Li, Guohui Zhang, Xiaoxiao Ma, Yiting Lu, Lin Chen, Zehui Che …
  • Published date2026/10/09

Categories

More Items

Benchmark for evaluating agents' ability to deliver working AI systems in realistic customer environments: 95 end-to-end delivery tasks with scripted stakeholders and production-like systems, scored by improvement to the customer's business metric and penalized for stakeholder contact.

Hugging Face

Benchmark package for evaluating visual agents that produce editable 3D Blender projects from real-world text, image, and video briefs; includes 223 tasks, 2,651 binary rubrics, Harbor-compatible inputs with Base64 previews, and multi-view render verification.

Hugging Face

Evaluates AI agents on end-to-end forward-deployed engineering tasks by providing 95 expert-adapted scenarios where agents operate in simulated customer environments, interact with stakeholders, and are graded on business uplift minus stakeholder contact cost.