DeepVerse: 4D Autoregressive Video Generation as a World Model

• URL: http://arxiv.org/abs/2506.01103v1
• Date: Sun, 01 Jun 2025 17:58:36 GMT
• Title: DeepVerse: 4D Autoregressive Video Generation as a World Model
• Authors: Junyi Chen, Haoyi Zhu, Xianglong He, Yifan Wang, Jianjun Zhou, Wenzheng Chang, Yang Zhou, Zizun Li, Zhoujie Fu, Jiangmiao Pang, Tong He,
• Abstract summary: We introduce DeepVerse, a novel 4D interactive world model explicitly incorporating geometric predictions from previous timesteps into current predictions on actions.<n> Experiments demonstrate that by incorporating explicit geometric constraints, DeepVerse captures richer-temporal relationships and underlying physical dynamics.<n>This capability significantly reduces drift and enhances temporal consistency, enabling the model to reliably generate extended future sequences.
• Score: 16.877309608945566
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: World models serve as essential building blocks toward Artificial General Intelligence (AGI), enabling intelligent agents to predict future states and plan actions by simulating complex physical interactions. However, existing interactive models primarily predict visual observations, thereby neglecting crucial hidden states like geometric structures and spatial coherence. This leads to rapid error accumulation and temporal inconsistency. To address these limitations, we introduce DeepVerse, a novel 4D interactive world model explicitly incorporating geometric predictions from previous timesteps into current predictions conditioned on actions. Experiments demonstrate that by incorporating explicit geometric constraints, DeepVerse captures richer spatio-temporal relationships and underlying physical dynamics. This capability significantly reduces drift and enhances temporal consistency, enabling the model to reliably generate extended future sequences and achieve substantial improvements in prediction accuracy, visual realism, and scene rationality. Furthermore, our method provides an effective solution for geometry-aware memory retrieval, effectively preserving long-term spatial consistency. We validate the effectiveness of DeepVerse across diverse scenarios, establishing its capacity for high-fidelity, long-horizon predictions grounded in geometry-aware dynamics.

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