5 papers
MegaParts: Scaling Part-Aware 3D Object Generation to 300 Parts via Token-Efficient Autoregressive Modeling
Manwen Liao, Xinyu Lian, Jian Mao +11
Part-aware 3D object generation is essential for graphics applications such as controllable modeling, editing, and articulation, where objects are represented as coherent assemblie…
Pair2Scene: Learning Local Object Relations for Procedural Scene Generation
Xingjian Ran, Shujie Zhang, Weipeng Zhong +2
Generating high-fidelity 3D indoor scenes remains a significant challenge due to data scarcity and the complexity of modeling intricate spatial relations. Current methods often str…
InternScenes: A Large-scale Simulatable Indoor Scene Dataset with Realistic Layouts
Weipeng Zhong, Peizhou Cao, Yichen Jin +9
The advancement of Embodied AI heavily relies on large-scale, simulatable 3D scene datasets characterized by scene diversity and realistic layouts. However, existing datasets typic…
MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds
Bingquan Dai, Li Ray Luo, Qihong Tang +9
Reconstructing 3D objects into editable programs is pivotal for applications like reverse engineering and shape editing. However, existing methods often rely on limited domain-spec…
Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated Objects via Procedural Generation
Xinyu Lian, Zichao Yu, Ruiming Liang +9
Large-scale articulated objects with high quality are desperately needed for multiple tasks related to embodied AI. Most existing methods for creating articulated objects are eithe…