Generalizing real-time physics-based animation across diverse 3D shapes and spatial discretizations remains challenging. In this paper, we introduce PhysSkin++, a physics-informed framework that learns continuous neural skinning fields from static geometries to construct compact deformation subspaces. Specifically, we...
Yuan-Hang Lei, Bao-Fei Liu, Xing-Xuan Li et al.· 0 citations
Feed-forward Gaussian reconstruction builds 3D scenes directly from input images for novel view rendering, but learning Gaussian attributes from predicted geometry for high-quality rendering under large viewpoint changes remains challenging. We analyze limitations in Gaussian attribute learning and introduce InfiniSpla...
Yong-Zhen Hu, Hao Yu, Jia-Wei Wang et al.· 0 citations
General-purpose service robots need navigation systems that can handle diverse human requests in unfamiliar environments, combining task generality with scene generality. Some existing methods fine-tune multimodal large language models (MLLMs) to predict navigation actions, making their behavior dependent on the covera...
Jin-Kai Zhang, Jing-Yi Xu, Yuan-Hong Yu et al.· 0 citations
InfiniSplat is presented, a feed-forward single-image 3DGS framework that moves from a pixel-aligned representation toward a surface-aligned representation, and achieves state-of-the-art performance compared with single-image feed-forward baselines, and demonstrates zero-shot generalization from Hypersim indoor synthet...
Jiawei Wang, Hao Yu, Yongzhen Hu et al.· 0 citations
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