# WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation

> WB ENCH is a comprehensive multi-turn benchmark that reveals no single interactive video world model excels across all five key dimensions of quality, adherence, consistency, and physics.

- **Source:** [arXiv](https://arxiv.org/abs/2605.25874)
- **Published:** 2026-05-27
- **Permalink:** https://picx.dev/p/5G7Me4
- **Whiteboard:** https://picx.dev/p/5G7Me4/image

## Summary

# WB ENCH: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation

## Summary (Overview)
*   **Unified Benchmark:** Introduces WB ENCH, a comprehensive benchmark for evaluating interactive video world models across five key dimensions: Video Quality, Setting Adherence, Interaction Adherence, Consistency, and Physics Compliance.
*   **Multi-turn Dataset:** Contains 289 test cases with 1,058 interaction turns, covering diverse scenes, styles, subjects, perspectives (first- and third-person), and four interaction types (Navigation, Subject Action, Event Editing, Perspective Switching).
*   **Unified Navigation Control:** Supports fair cross-paradigm comparison by representing navigation actions in three aligned forms: text, camera pose (6-DoF), and discrete keyboard actions.
*   **Diagnostic Evaluation:** Uses 22 fine-grained automatic sub-metrics combining specialist vision models and large multimodal models (LMMs), validated against human judgments. Evaluation of 20 state-of-the-art models reveals no single model excels across all dimensions.
*   **Key Findings:** Navigation capability is largely independent of other dimensions; camera control does not guarantee perspective consistency; physical correctness correlates more with rendering quality than control; and navigation performance degrades most severely over multiple turns.

## Introduction and Theoretical Foundation
Recent advances in video generation have enabled **interactive world models** that simulate environment evolution in response to user actions. These models function as conditional generators predicting the next observation $o_{t+1}$ given history $o_{\le t}$ and actions $a_{\le t}$:
$$ o_{t+1} \sim f_\theta(o_{t+1} \mid o_{\le t}, a_{\le t}) $$
A capable interactive world model must fulfill five complementary roles analogous to a game engine: **Renderer** (visual quality), **Director** (world initialization), **Controller** (interaction execution), **Memory** (state preservation), and **Engine** (physical compliance).

Existing benchmarks are fragmented, focusing on isolated aspects. Non-interactive suites (e.g., VBench) assess video quality without control. World model benchmarks (e.g., WorldMark, MIND, Omni-WorldBench) cover navigation and memory but lack semantic interactions or are restricted to specific domains (e.g., autonomous driving in WorldLens). **No existing benchmark jointly covers diverse open-domain scenes, both perspectives, a comprehensive interaction taxonomy, and multi-turn closed-loop evaluation.**

WB ENCH fills this gap by providing a unified framework that decomposes evaluation into explicit **World Settings** $W$ (defining the initial state $o_0$) and **Interaction sequences** $I = (a_0, a_1, ..., a_{T-1})$ (specifying user controls over $T$ turns). This separation makes failure modes easier to diagnose.

## Methodology

### 3.1 Dataset Construction
Each test case is defined by a **World Setting** $W$ and a multi-turn **Interaction** sequence $I$.
*   **World Setting Attributes:**
    1.  **Scene:** Environment type, layout, and dynamics (e.g., terrain, buildings).
    2.  **Style:** Rendering appearance (e.g., realistic, cartoon, oil painting).
    3.  **Perspective:** First-person (FPP) or third-person (TPP).
    4.  **Subject:** Primary entity (e.g., human, animal, vehicle). Applies to all TPP and relevant FPP cases.
*   **Interaction Types:**
    1.  **Navigation:** Camera/ego-agent motion via unified controls (W/S/A/D for translation, ←/→/↑/↓ for rotation).
    2.  **Subject Action:** Actions performed by the primary subject (manipulation, locomotion, tool use, combat, gesture).
    3.  **Event Editing:** Externally imposed environment changes (weather, time-of-day, object appearances).
    4.  **Perspective Switching:** Transitions between FPP and TPP views.
*   **Construction:** Follows a *setting-first* principle. Annotators design a coherent world setting and then derive physically executable, semantically coherent interaction sequences. Cases undergo manual review.

### 3.2 Dataset Statistics
WB ENCH comprises **289 cases** spanning **1,058 interaction turns**.
*   **Perspective:** 62% FPP, 38% TPP.
*   **Interaction Distribution:** Navigation (57%), Subject Action (20%), Event Editing (17%), Perspective Switching (6%).
*   **Scene Diversity:** Nature (31%), Urban (21%), Indoor (17%), Workspace (13%), Fantasy (10%), Sports (8%).
*   **Subject Diversity:** Human (64%), Animal (9%), Robot (9%), Vehicle (7%), Other (10%).
*   **Style:** Photorealistic (52%), Styled (48% e.g., anime, cartoon, oil painting).
*   **Turn Depth:** Average 3.7 turns per case (range 2-9).

### 4. WB ENCH Evaluation Suite
Evaluation is decomposed into **five dimensions** with **22 fine-grained sub-metrics**. All scores are linearly rescaled to $[0, 100]$.

1.  **Video Quality (6 metrics):** Aesthetic Quality, Imaging Quality, Temporal Flickering, Dynamic Degree, Motion Smoothness (from VBench), and HPSv3-Norm (human-preference score).
2.  **Setting Adherence (2 metrics):**
    *   **S.1 Scene Adherence:** VLM evaluates consistency of initially visible elements and appearance of described offscreen elements.
    *   **S.2 Subject Adherence:** VLM evaluates match of subject's visual attributes and movement style to description.
3.  **Interaction Adherence (4 metrics):**
    *   **I.1 Navigation Score:** Compares MegaSaM-estimated camera poses against a synthetic ground-truth trajectory. Computes normalized Absolute Trajectory Error (nATE) and cross-turn consistency.
    *   **I.2 Event Editing & I.3 Subject Action Adherence:** Turn-level VLM protocol with five binary checks per turn (change detection, event occurrence, completion, detail accuracy, anomaly absence). Score is average of $[0,

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