1 citations · 1 across the 11 of their papers we have counts for
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Learning Primitive Embodied World Models: Towards Scalable Robotic Learning
Qiao Sun, Liujia Yang, Wei Tang +12
While video-generation-based embodied world models have gained increasing attention, their reliance on large-scale embodied interaction data remains a key bottleneck. The scarcity,…
: Permutation-Equivariant Visual Geometry Learning
Yifan Wang, Jianjun Zhou, Haoyi Zhu +7
We introduce , a feed-forward neural network that offers a novel approach to visual geometry reconstruction, breaking the reliance on a conventional fixed reference view. Prev…
VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers
Yating Wang, Haoyi Zhu, Mingyu Liu +3
In this paper, we introduce an innovative vector quantization based action tokenizer built upon the largest-scale action trajectory dataset to date, leveraging over 100 times more…
DeepVerse: 4D Autoregressive Video Generation as a World Model
Junyi Chen, Haoyi Zhu, Xianglong He +8
World models serve as essential building blocks toward Artificial General Intelligence (AGI), enabling intelligent agents to predict future states and plan actions by simulating co…
CoMo: Learning Continuous Latent Motion from Internet Videos for Scalable Robot Learning
Jiange Yang, Yansong Shi, Haoyi Zhu +6
Unsupervised learning of latent motion from Internet videos is crucial for robot learning. Existing discrete methods generally mitigate the shortcut learning caused by extracting e…
Aether: Geometric-Aware Unified World Modeling
Aether Team, Haoyi Zhu, Yifan Wang +8
The integration of geometric reconstruction and generative modeling remains a critical challenge in developing AI systems capable of human-like spatial reasoning. This paper propos…