4 papers
PanDA: Towards Panoramic Depth Anything with Unlabeled Panoramas and Mobius Spatial Augmentation
Zidong Cao, Jinjing Zhu, Weiming Zhang +4
Recently, Depth Anything Models (DAMs) - a type of depth foundation models - have demonstrated impressive zero-shot capabilities across diverse perspective images. Despite its succ…
SEAL: SEmantic-Augmented Imitation Learning via Language Model
Chengyang Gu, Yuxin Pan, Haotian Bai +2
Hierarchical Imitation Learning (HIL) is a promising approach for tackling long-horizon decision-making tasks. While it is a challenging task due to the lack of detailed supervisor…
CompoNeRF: Text-guided Multi-object Compositional NeRF with Editable 3D Scene Layout
Haotian Bai, Yuanhuiyi Lyu, Lutao Jiang +4
Text-to-3D form plays a crucial role in creating editable 3D scenes for AR/VR. Recent advances have shown promise in merging neural radiance fields (NeRFs) with pre-trained diffusi…
High-Fidelity Mask-free Neural Surface Reconstruction for Virtual Reality
Haotian Bai, Yize Chen, Lin Wang
Object-centric surface reconstruction from multi-view images is crucial in creating editable digital assets for AR/VR. Due to the lack of geometric constraints, existing methods, e…