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<a href="https://taiminglu.com/" target="_blank">Taiming Lu</a>,
<a href="https://www.tshu.io/" target="_blank">Tianmin Shu</a>,
<a href="https://lambert-x.github.io/" target="_blank">Junfei Xiao</a>,
<span>Luoxin Ye</span>,
<a href="https://jiahaoplus.github.io/" target="_blank">Jiahao Wang</a>,
<a href="https://sites.google.com/view/cheng-peng/" target="_blank">Cheng Peng</a>,
<a href="https://weichen582.github.io/" target="_blank">Chen Wei</a>, <br>
<a href="https://danielkhashabi.com/" target="_blank">Daniel Khashabi</a>,
<a href="https://engineering.jhu.edu/faculty/rama-chellappa/" target="_blank">Rama Chellappa</a>,
<a href="https://www.cs.jhu.edu/~ayuille/" target="_blank">Alan Yuille</a>,
<a href="https://beckschen.github.io/" target="_blank">Jieneng Chen</a>
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<h2 class="paper_section_body">Planning with partial observation is a central challenge in embodied AI. A
majority of prior works have tackled this challenge by developing agents that physically explore their
environment to update their beliefs about the world state. However, humans can imagine unseen parts of the
world through a mental exploration and revise their beliefs with imagined observations. Such updated beliefs
can allow them to make more informed decisions at the current step, without having to physically explore the
world first. To achieve this human-like ability, we introduce the Generative World Explorer (GenEx), a video
generation model that allows an agent to mentally explore a large-scale 3D world (e.g., urban scenes) and
acquire imagined observations to update its belief about the world . </h2>
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<strong>To support exploration, we design a variant of video generation model. </strong> It is defined as
<em>f<sub>θ</sub>: ℝ<sup>H
× W</sup> → ℝ<sup>T × H × W</sup></em>
that takes an input image with height <em>H</em> and width <em>W</em> and outputs a sequence of <em>T</em>
images as predicted video.
We aim to generate realistic 360-degree panoramic video sequences that simulate forward movement through any
environment by leveraging image-to-video diffusion models <em>f<sub>θ</sub></em>.
We introduce our diffuser architecture and designed spherical consistent learning for panoramic navigataion.
</p>
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<p>
<strong>Our trained diffuser seamlessly adapts as a world explorer, to support navigation in a imaginative
world.</strong> We use LMMs, like GPT-4o, as agents controlling movement through an egocentric view. The
agent can move forward or change direction, with forward movement generated by our video generator, and
angle changes handled by the panoramic image. The agent can perform unlimited actions, making our diffuser a
generative world explorer.
</p>
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<h2 class="paper_section_title">Exploration Consistency</h2>
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<p>
<strong>To ensure generation quality, we introduce navigational cycle consistency.</strong> GenEx navigates
a randomly sampled closed path, returning to the origin. In optimal cases, the start and end views are
identical, ensuring consistency in world modeling.
</p>
</div>
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<h2 class="paper_section_title">GenEx for Embodied AI</h2>
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<p>
<strong>GenEx gives a new level of intelligence to embodied AI agents.</strong>
For a single-agent scenario involving decision-making, picture yourself driving down a street when a siren
sounds, but you can’t see the source. With GenEx, you project what’s ahead. It reveals an ambulance just
around the corner. You stop crossing the street to make space for it to pass.
For a multi-agent scenario involving interaction, suppose you are waiting at a red light. You see a
pedestrian and an approaching car, and everything seems fine. But GenEx helps you recognize that the
pedestrian can’t see the car. Likewise, the car can’t see the pedestrian. Your vehicle is blocking their
views. Realizing this, you act immediately to warn them, preventing a collision.
</p>
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<pre class="bibtex-text" id="bibtexText">
@misc{lu2024generativeworldexplorer,
title={Generative World Explorer},
author={Taiming Lu and Tianmin Shu and Alan Yuille and Daniel Khashabi and Jieneng Chen},
year={2024},
eprint={2411.11844},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2411.11844},
}
</pre>
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