Using reinforcement learning to post-train joint video-audio generation models requires a reward signal. Existing methods construct this reward by combining metrics for individual quality dimensions, including audio quality, visual fidelity, and synchronization. However, these metrics evaluate perceptual dimensions sep...
Yin-Ming Huang, Shu-Yuan Tu, Xi Yan et al.· 4 citations
Emerging World-Action Models (WAMs) have demonstrated promising performance in autonomous driving by jointly modeling future driving scene evolution and trajectory planning. However, existing WAMs are typically trained with video data, which is only 2D projections of the underlying 4D driving scene. Consequently, WAMs...
Jiacheng Fu, Yibo Yuan, Meng Tian et al.· 0 citations
End-to-end driving requires a coherent understanding of future scenes, yet existing methods model these scenes using task-specific heads and output formats, with limited scalability. Can video generation instead provide a shared predictor? We introduce SUV, a unified end-to-end driving framework that casts future Scene...
Yibo Yuan, Jiacheng Fu, Jiangtong Zhu et al.· 0 citations
Recent multimodal large language models (MLLMs) have made remarkable progress on fine-grained perception tasks under the"Thinking with Images"(TwI) paradigm by iteratively performing various visual tool operations. However, this paradigm relies heavily on frequent external tool calls and repeated image re-encoding, whi...
Xiuwei Chen, Quanlin Chen, Wentao Hu et al.· 0 citations
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