TY - JOUR
T1 - LiveScene
T2 - 38th Conference on Neural Information Processing Systems, NeurIPS 2024
AU - Qu, Delin
AU - Chen, Qizhi
AU - Zhang, Pingrui
AU - Gao, Xianqiang
AU - Zhao, Bin
AU - Wang, Zhigang
AU - Wang, Dong
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2024 Neural information processing systems foundation. All rights reserved.
PY - 2024
Y1 - 2024
N2 - This paper scales object-level reconstruction to complex scenes, advancing interactive scene reconstruction. We introduce two datasets, OmniSim and InterReal, featuring 28 scenes with multiple interactive objects. To tackle the challenge of inaccurate interactive motion recovery in complex scenes, we propose LiveScene, a scene-level language-embedded interactive radiance field that efficiently reconstructs and controls multiple objects. By decomposing the interactive scene into local deformable fields, LiveScene enables separate reconstruction of individual object motions, reducing memory consumption. Additionally, our interaction-aware language embedding localizes individual interactive objects, allowing for arbitrary control using natural language. Our approach demonstrates significant superiority in novel view synthesis, interactive scene control, and language grounding performance through extensive experiments. Project page: https://livescenes.github.io.
AB - This paper scales object-level reconstruction to complex scenes, advancing interactive scene reconstruction. We introduce two datasets, OmniSim and InterReal, featuring 28 scenes with multiple interactive objects. To tackle the challenge of inaccurate interactive motion recovery in complex scenes, we propose LiveScene, a scene-level language-embedded interactive radiance field that efficiently reconstructs and controls multiple objects. By decomposing the interactive scene into local deformable fields, LiveScene enables separate reconstruction of individual object motions, reducing memory consumption. Additionally, our interaction-aware language embedding localizes individual interactive objects, allowing for arbitrary control using natural language. Our approach demonstrates significant superiority in novel view synthesis, interactive scene control, and language grounding performance through extensive experiments. Project page: https://livescenes.github.io.
UR - http://www.scopus.com/inward/record.url?scp=105000466085&partnerID=8YFLogxK
M3 - 会议文章
AN - SCOPUS:105000466085
SN - 1049-5258
VL - 37
JO - Advances in Neural Information Processing Systems
JF - Advances in Neural Information Processing Systems
Y2 - 9 December 2024 through 15 December 2024
ER -