3 citations · 5 across the 9 of their papers we have counts for
11 papers
AlignMorph: Tuning-Free Diffusion Image Morphing via Explicit Semantic Transport
Wuyi Liu, Xu Han, Yuren Chen +3
Image morphing aims to produce a smooth and semantically consistent transition between two input images. Existing diffusion-based morphing methods either require expensive per-pair…
Long-Tailed 3D Point Cloud Dataset Distillation
Jiahao You, Xu Han, Jinfeng Xu +1
Dataset distillation compresses large-scale datasets into compact synthetic sets while preserving their training utility, enabling efficient 3D point cloud training. Current point…
LumiX: Structured and Coherent Text-to-Intrinsic Generation
Xu Han, Biao Zhang, Xiangjun Tang +2
We present LumiX, a structured diffusion framework for coherent text-to-intrinsic generation. Conditioned on text prompts, LumiX jointly generates a comprehensive set of intrinsic…
SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds
Jinfeng Xu, Xianzhi Li, Yuan Tang +5
Recent advancements in deep learning have greatly enhanced 3D object recognition, but most models are limited to closed-set scenarios, unable to handle unknown samples in real-worl…
MoST: Efficient Monarch Sparse Tuning for 3D Representation Learning
Xu Han, Yuan Tang, Jinfeng Xu +1
We introduce Monarch Sparse Tuning (MoST), the first reparameterization-based parameter-efficient fine-tuning (PEFT) method tailored for 3D representation learning. Unlike existing…
Fancy123: One Image to High-Quality 3D Mesh Generation via Plug-and-Play Deformation
Qiao Yu, Xianzhi Li, Yuan Tang +4
Generating 3D meshes from a single image is an important but ill-posed task. Existing methods mainly adopt 2D multiview diffusion models to generate intermediate multiview images,…
RIDE: Boosting 3D Object Detection for LiDAR Point Clouds via Rotation-Invariant Analysis
Zhaoxuan Wang, Xu Han, Hongxin Liu +1
The rotation robustness property has drawn much attention to point cloud analysis, whereas it still poses a critical challenge in 3D object detection. When subjected to arbitrary r…
More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding
Yuan Tang, Xu Han, Xianzhi Li +5
Enabling Large Language Models (LLMs) to comprehend the 3D physical world remains a significant challenge. Due to the lack of large-scale 3D-text pair datasets, the success of LLMs…
PointDreamer: Zero-shot 3D Textured Mesh Reconstruction from Colored Point Cloud
Qiao Yu, Xianzhi Li, Yuan Tang +4
Faithfully reconstructing textured meshes is crucial for many applications. Compared to text or image modalities, leveraging 3D colored point clouds as input (colored-PC-to-mesh) o…
MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors
Yuan Tang, Xu Han, Xianzhi Li +4
Large 2D vision-language models (2D-LLMs) have gained significant attention by bridging Large Language Models (LLMs) with images using a simple projector. Inspired by their success…
Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model
Xu Han, Yuan Tang, Zhaoxuan Wang +1
Existing Transformer-based models for point cloud analysis suffer from quadratic complexity, leading to compromised point cloud resolution and information loss. In contrast, the ne…