10 papers
GEM: Generative Supervision Helps Embodied Intelligence
Ruowen Zhao, Bangguo Li, Zuyan Liu +9
Embodied Vision-Language Models (VLMs) have demonstrated impressive performance and generalization in robotics, particularly within Vision-Language-Action frameworks. However, a si…
NANO3D: A Training-Free Approach for Efficient 3D Editing Without Masks
Junliang Ye, Shenghao Xie, Ruowen Zhao +5
3D object editing is essential for interactive content creation in gaming, animation, and robotics, yet current approaches remain inefficient, inconsistent, and often fail to prese…
ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding
Junliang Ye, Zhengyi Wang, Ruowen Zhao +2
Recently, the powerful text-to-image capabilities of ChatGPT-4o have led to growing appreciation for native multimodal large language models. However, its multimodal capabilities r…
DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning
Ruowen Zhao, Junliang Ye, Zhengyi Wang +4
Triangle meshes play a crucial role in 3D applications for efficient manipulation and rendering. While auto-regressive methods generate structured meshes by predicting discrete ver…
MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenization
Yiwen Chen, Yikai Wang, Yihao Luo +5
Meshes are the de facto 3D representation in the industry but are labor-intensive to produce. Recently, a line of research has focused on autoregressively generating meshes. This a…
LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models
Zhengyi Wang, Jonathan Lorraine, Yikai Wang +4
This work explores expanding the capabilities of large language models (LLMs) pretrained on text to generate 3D meshes within a unified model. This offers key advantages of (1) lev…