While multimodal large language models (MLLMs) achieve remarkable performance on generic image captioning, they frequently suffer from structural hallucinations in human-centric scenarios. Accurately modeling human subjects is foundational for critical downstream applications, such as accurate avatar/video/image genera...
Bozhou Li, Jia-Hang Zhang, Yue Ding et al.· 0 citations
Face-to-face audiovisual interaction is central to human communication, conveying rich emotional and social cues. However, existing multimodal dialogue datasets remain limited by inadequate emotion annotations, poor emotional diversity, and small scale. We introduce EmotionDialogCN, a large-scale audiovisual-emotional...
Video generation has advanced to produce visually compelling and temporally coherent results. Yet, whether these models can genuinely think with video--executing symbolic rules, respecting physical laws, and pursuing intentional goals--remains an open question. Existing benchmarks only partially address this, often con...
Meng Luo, Yi-Chen Liu, Jia-Hao Wang et al.· 0 citations
StreamHOI, a low-latency streaming framework for long-duration HOI video generation, finds that the standard sink-local memory design faces a trade-off in streaming HOI generation, and different transformer blocks show different historical-memory preferences for HOI regions and surrounding regions.
Ze-Jing Rao, Haoxian Zhang, Xiao-Qiang Liu et al.· arXiv.org· 2 citations
Video motion transfer aims to animate a target object using dynamics from a reference video. Existing formulations largely rely on fixed structural correspondence, which becomes ill-defined when reference and target objects differ substantially in morphology, articulation, or deformation mechanisms. We introduce Motion...
Zhixue Fang, Zhimin Zhang, Bi'an Du et al.· 0 citations
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