Zekun Qi

I am a final-year MS student joint study in Xi’an Jiaotong University & IIIS, Tsinghua University under the supervision of Prof. Andrew C. Yao. I collaborate closely with Prof. Kaisheng Ma, Prof. Li Yi, Prof. He Wang and Runpei Dong.
In 2022, I obtained my bachelor’s degree in Automation from Xi’an Jiaotong University .

My research focuses on 3D Computer Vision, Multimodal Large Language Models, and Embodied AI.

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Publications

* indicates equal contribution

We rethink the role of positional encoding in 3D representation learning, and propose Positional Prompt Tuning, a simple but efficient method for transfer learning.

We collect diverse images and prompts, and utilize GPT-4o for automated evaluation aligned with human preference.

We present ShapeLLM, the first 3D Multimodal Large Language Model designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages.

We present DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models empowered with frequently overlooked synergy between multimodal comprehension and creation.

We achieve rapid, multi-category 3D conditional generation by sharing the merits of different representations. VPP can generate 3D shapes less than 0.2s using a single RTX 2080Ti.

We enhance the utilization of color information to improve 3D scene self-supervised learning.

We propose contrast guided by reconstruct to mitigate the pattern differences between two self-supervised paradigms.

We propose to use autoencoders as cross-modal teachers to transfer dark knowledge into 3D representation learning.

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