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<section class="fp-section sec-hero" data-bg="dark">
<h1 class="publication-title">
<span class="t-main" style="font-size: 4rem;">WildWorld</span><br>
<span class="t-main">A Large-Scale Dataset for Dynamic World Modeling<br>
with </span><span class="t-red">Actions</span><span class="t-main"> and </span><span class="t-blue">Explicit State</span><span class="t-main"> toward </span><span class="t-orange">Generative ARPG</span>
</h1>
<div class="publication-authors">
<span class="author-block">Zhen Li<sup> 1,2,4,*</sup></span>,
<span class="author-block">Zian Meng<sup> 1,3,*</sup></span>,
<span class="author-block">Shuwei Shi<sup> 1</sup></span>,
<span class="author-block">Wenshuo Peng<sup> 5</sup></span>,<br>
<span class="author-block">Yuwei Wu<sup> 2,4,📧</sup></span>,
<span class="author-block">Bo Zheng<sup> 1</sup></span>,
<span class="author-block">Chuanhao Li<sup> 1,📧</sup></span>,
<span class="author-block">Kaipeng Zhang<sup> 1,📧</sup></span>
<div class="affiliations">
<sup>1</sup>Alaya Studio, Shanda AI Research Tokyo,
<sup>2</sup>Beijing Institute of Technology,
<sup>3</sup>Shanghai Innovation Institute<br>
<sup>4</sup>Shenzhen MSU-BIT University,
<sup>5</sup>Tsinghua University
</div>
<div class="corresponding">
📧 Corresponding authors: wuyuwei@bit.edu.cn; chuanhao.li@shanda.com; kaipeng.zhang@shanda.com
</div>
</div>
<div class="publication-links">
<a href="https://arxiv.org/abs/2603.23497" target="_blank" class="pub-btn">
<i class="ai ai-arxiv"></i> Paper
</a>
<a href="https://github.com/ShandaAI/WildWorld" target="_blank" class="pub-btn">
<i class="fab fa-github"></i> Code
</a>
<span class="pub-btn disabled">🤗 Dataset (Coming Soon)</span>
</div>
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Scroll to explore
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</section>
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</div>
<div class="video-hint">🔊 Watch the WildWorld introduction video | <a href="https://www.youtube.com/watch?v=9vcSg553r2g" target="_blank" style="color:#3274D8; text-decoration:none;">Watch on YouTube →</a></div>
</section>
<section class="fp-section sec-content" data-bg="light">
<div class="section-inner">
<h2 class="section-title">Introduction</h2>
<div class="section-text">
<p>Learning a predictive world model requires inferring latent states and modeling their action-conditioned transitions. However, existing datasets typically lack diverse action spaces, and actions are directly tied to visual observations rather than mediated by underlying states.</p>
<p>In many scenarios, actions manifest through <b>implicit state transitions</b> rather than explicit visual changes. For example, the action "shoot" affects internal state variables like "remaining ammunition", which cannot be reliably inferred from pixels alone, yet crucially determines future visual outcomes.</p>
<p><b>WildWorld</b> addresses this by providing a large-scale action-conditioned world modeling dataset with <b>explicit state annotations</b>, automatically collected from <em>Monster Hunter: Wilds</em> — a photorealistic AAA action RPG. It contains RGB frames with aligned depth maps, camera poses, skeleton, and action / state ground truth. We provide both fine-grained action-level captions and sample-level captions, making the dataset applicable to various experimental settings.</p>
</div>
<div class="figure-container">
<img src="https://cdn.jsdelivr.net/gh/ShandaAI/wildworld-project@0.1.0/static/images/teaser.png" alt="WildWorld multi-modal annotations">
</div>
</div>
</section>
<section class="fp-section sec-content" data-bg="light">
<div class="section-inner">
<h2 class="section-title">Dataset Construction</h2>
<div class="section-text">
<p>We design a fully automated pipeline with four stages: (1) <b>Automated Game Play</b> via programmatic UI navigation and behavior-tree-driven AI; (2) <b>Multi-Stream Recording</b> with timestamp-embedded frame-wise synchronization; (3) <b>Data Processing & Filtering</b> that removes short clips, shot-cuts, stuttering, extreme luminance, and occlusions; (4) <b>Hierarchical Captioning</b> with action-level captions grounded in state ground truth and sample-level summaries via LLM.</p>
</div>
<div class="figure-container">
<img src="https://cdn.jsdelivr.net/gh/ShandaAI/wildworld-project@0.1.0/static/images/framework-arxiv.png" alt="WildWorld dataset curation pipeline">
</div>
</div>
</section>
<section class="fp-section sec-content" data-bg="light">
<div class="section-inner">
<h2 class="section-title">Dataset Overview</h2>
<div class="section-text">
<p>WildWorld contains <b>108M+ annotated frames</b> with <b>450+ action types</b> across <b>29 monster species</b>, 4 player characters, 4 weapon types, and 5 distinct open-world stages. The dataset covers 66% combat and 34% traversal scenarios with a natural long-tail action distribution.</p>
</div>
<div class="figure-container">
<img src="https://cdn.jsdelivr.net/gh/ShandaAI/wildworld-project@0.1.0/static/images/dataset_overview_figure.png" alt="Dataset statistics overview">
</div>
</div>
</section>
<section class="fp-section sec-content" data-bg="light">
<div class="section-inner">
<h2 class="section-title">WildBench</h2>
<div class="section-text">
<p>We propose <b>WildBench</b>, a comprehensive benchmark with 200 curated test samples evaluating interactive world models from four perspectives:</p>
<ul>
<li><b>Video Quality</b> — Motion Smoothness, Dynamic Degree, Aesthetic Quality, Image Quality (VBench).</li>
<li><b>Camera Control</b> — Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) via SfM.</li>
<li><b>Action Following</b> — Whether generated videos faithfully reflect input actions.</li>
<li><b>State Alignment</b> — Alignment between generated and ground truth state evolution via keypoint tracking.</li>
</ul>
</div>
</div>
</section>
<section class="fp-section sec-content" data-bg="light" style="height: auto; min-height: 100vh;">
<div class="section-inner">
<h2 class="section-title">Experiments</h2>
<div class="section-text">
<p>We evaluate several interactive video generation approaches on WildWorld under different conditioning settings:</p>
<ul>
<li><b>Baseline</b> — Wan2.2-TI2V-5B, text+image-to-video generation without explicit control signals.</li>
<li><b>CamCtrl</b> — Camera-conditioned generation using ground-truth per-frame camera poses.</li>
<li><b>SkelCtrl</b> — Skeleton-conditioned generation taking rendered skeleton pose videos.</li>
<li><b>StateCtrl</b> — State-conditioned generation using camera, skeletons, and world states jointly.</li>
<li><b>StateCtrl-AR</b> — Autoregressive variant predicting states from the first frame only.</li>
</ul>
</div>
<div class="figure-container figure-group">
<img src="https://cdn.jsdelivr.net/gh/ShandaAI/wildworld-project@0.1.0/static/images/table1.png" alt="Quantitative results">
<p class="table-caption">Quantitative comparison of different approaches trained on WildWorld and evaluated on WildBench.</p>
</div>
<div class="figure-container figure-group fig-2">
<img src="https://cdn.jsdelivr.net/gh/ShandaAI/wildworld-project@0.1.0/static/images/qualitative_comparison_v2.png" alt="Qualitative comparison">
<p class="table-caption">Qualitative comparison of different approaches trained on WildWorld and evaluated on WildBench.</p>
</div>
</div>
</section>
<section class="fp-section sec-content" data-bg="light" style="min-height: auto; height: auto;">
<div class="section-inner">
<h2 class="section-title">BibTeX</h2>
<pre><code>@misc{li2026wildworldlargescaledatasetdynamic,
title={WildWorld: A Large-Scale Dataset for Dynamic World Modeling with Actions and Explicit State toward Generative ARPG},
author={Zhen Li and Zian Meng and Shuwei Shi and Wenshuo Peng and Yuwei Wu and Bo Zheng and Chuanhao Li and Kaipeng Zhang},
year={2026},
eprint={2603.23497},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2603.23497},
}</code></pre>
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var container = document.getElementById('fullpage');
var sections = document.querySelectorAll('.fp-section');
var total = sections.length;
var current = 0;
var isAnimating = false;
var LOCK_MS = 800;
var nav = document.getElementById('page-nav');
for (var i = 0; i < total; i++) {
var dot = document.createElement('button');
dot.className = 'nav-dot';
dot.setAttribute('data-index', i);
dot.addEventListener('click', function() {
goTo(parseInt(this.getAttribute('data-index')));
});
nav.appendChild(dot);
}
var dots = nav.querySelectorAll('.nav-dot');
function updateDots() {
for (var i = 0; i < dots.length; i++) {
dots[i].classList.remove('active', 'dark');
if (i === current) dots[i].classList.add('active');
if (sections[current].getAttribute('data-bg') === 'light') {
dots[i].classList.add('dark');
}
}
}
function activateSection(index) {
sections[index].classList.add('phase-0');
var inner = sections[index].querySelector('.section-inner');
if (inner) inner.scrollTop = 0;
}
function deactivateSection(index) {
sections[index].classList.remove('phase-0');
}
function goTo(index) {
if (index < 0 || index >= total || index === current || isAnimating) return;
isAnimating = true;
deactivateSection(current);
current = index;
container.style.transform = 'translateY(' + (-current * 100) + 'vh)';
updateDots();
setTimeout(function() {
activateSection(current);
}, 300);
setTimeout(function() {
isAnimating = false;
}, LOCK_MS);
}
function canScrollInner(section, direction) {
var inner = section.querySelector('.section-inner');
if (!inner) return false;
var scrollable = inner.scrollHeight > inner.clientHeight + 2;
if (!scrollable) return false;
if (direction > 0) {
return inner.scrollTop + inner.clientHeight < inner.scrollHeight - 2;
} else {
return inner.scrollTop > 2;
}
}
var wheelAccumulator = 0;
var wheelThreshold = 50;
var wheelTimer = null;
window.addEventListener('wheel', function(e) {
e.preventDefault();
if (isAnimating) return;
var section = sections[current];
var direction = e.deltaY > 0 ? 1 : -1;
if (canScrollInner(section, direction)) {
var inner = section.querySelector('.section-inner');
inner.scrollTop += e.deltaY;
return;
}
wheelAccumulator += Math.abs(e.deltaY);
clearTimeout(wheelTimer);
wheelTimer = setTimeout(function() { wheelAccumulator = 0; }, 200);
if (wheelAccumulator >= wheelThreshold) {
wheelAccumulator = 0;
goTo(current + direction);
}
}, { passive: false });
var touchStartY = 0;
var touchMoved = false;
window.addEventListener('touchstart', function(e) {
touchStartY = e.touches[0].clientY;
touchMoved = false;
}, { passive: true });
window.addEventListener('touchmove', function(e) {
if (isAnimating) return;
var deltaY = touchStartY - e.touches[0].clientY;
var direction = deltaY > 0 ? 1 : -1;
var section = sections[current];
if (canScrollInner(section, direction)) {
return;
}
if (!touchMoved && Math.abs(deltaY) > 50) {
touchMoved = true;
goTo(current + direction);
}
}, { passive: true });
window.addEventListener('keydown', function(e) {
if (isAnimating) return;
if (e.key === 'ArrowDown' || e.key === 'PageDown' || e.key === ' ') {
e.preventDefault();
var section = sections[current];
if (canScrollInner(section, 1)) {
var inner = section.querySelector('.section-inner');
inner.scrollTop += 120;
} else {
goTo(current + 1);
}
} else if (e.key === 'ArrowUp' || e.key === 'PageUp') {
e.preventDefault();
var section2 = sections[current];
if (canScrollInner(section2, -1)) {
var inner2 = section2.querySelector('.section-inner');
inner2.scrollTop -= 120;
} else {
goTo(current - 1);
}
}
});
updateDots();
setTimeout(function() {
activateSection(0);
}, 100);
})();
</script>
</body>
</html>