BehaviorWorldGen is introduced, a framework that closes the loop between action models and world simulators through controllable behavior-aware structured world generation through controllable behavior-aware structured world generation.
Jia-Qi Wang, Zhuo Zhang, Hai-Ning Guan et al.· 0 citations
Modern driving action models are increasingly improved in a self-improvement loop, where a learned world simulator imagines future observations and the resulting data is fed back to refine the action model. However, the bottleneck of this loop lies in the simulators'inability to generate behaviorally plausible response...
Jiaqi Wang, Zhuo Zhang, Hai-Ning Guan et al.· 0 citations
This work proposes a foundation-feature Gaussian driving world model that unifies scene understanding, language-grounded reasoning, controllable 4D editing, and multi-modal generation within a single framework and introduces a foundation-feature Gaussian tokenizer that directly distills Qwen/SigLIP visual-language feat...
Tianchen Deng, Xue-Feng Chen, Shuang Wu et al.· 0 citations
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