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cs.CV2026
What Makes a Good Visual Representation for Spatial Understanding in MLLMs?
Xiaohu Huang, Yumin Guo, Kai Han
Spatial understanding is a necessary step in moving multimodal large language models (MLLMs) toward reasoning about the 3D physical world. Their visual pretraining data, however, i…
cs.CV2026
Beyond Single Expert: Harmonizing Diverse Visual Priors in MLLMs for Spatial Understanding
Xiao Lin, Xiaohu Huang, Kai Han
Multimodal Large Language Models (MLLMs) have demonstrated substantial promise in spatial understanding. Existing works typically incorporate prior knowledge extracted from a pre-t…
cs.CV2026
OmniShow: Unifying Multimodal Conditions for Human-Object Interaction Video Generation
Donghao Zhou, Guisheng Liu, Hao Yang +9
In this work, we study Human-Object Interaction Video Generation (HOIVG), which aims to synthesize high-quality human-object interaction videos conditioned on text, reference image…