1 citations · 1 across the 7 of their papers we have counts for
8 papers
Exemplar2VQA: A Scalable Exemplar-Driven Visual Question Answering Generation Framework via Multi-Agent Coding
Jiayu Ying, Qijian Tian, Ruijie Xu +4
Advancing spatial intelligence in Multimodal Large Language Models (MLLMs) is bottlenecked by the scarcity of complex, scalable 3D question-answer (QA) data. While manual annotatio…
GroupForward: Building Referable 3D Scenes via Instance-Grouped Feed-Forward Gaussian Splatting
Qijian Tian, Zimeng Wu, Xuhong Wang +2
Simultaneously reconstructing and understanding 3D environments is essential for embodied agents. Toward this goal, feed-forward semantic 3D Gaussian Splatting (3DGS) efficiently c…
DailyArt: Discovering Articulation from Single Static Images via Latent Dynamics
Hang Zhang, Qijian Tian, Jingyu Gong +4
Articulated objects are essential for embodied AI and world models, yet inferring their kinematics from a single closed-state image remains challenging because crucial motion cues…
S2D: Sparse to Dense Lifting for 3D Reconstruction with Minimal Inputs
Yuzhou Ji, Qijian Tian, He Zhu +5
Explicit 3D representations have already become an essential medium for 3D simulation and understanding. However, the most commonly used point cloud and 3D Gaussian Splatting (3DGS…
FLEG: Feed-Forward Language Embedded Gaussian Splatting from Any Views via Compact Semantic Representation
Qijian Tian, Xin Tan, Jiayu Ying +3
We present FLEG, a feed-forward network that reconstructs language-embedded 3D Gaussians from arbitrary views. Previous feed-forward language-embedded Gaussian reconstruction metho…
UniForward: Unified 3D Scene and Semantic Field Reconstruction via Feed-Forward Gaussian Splatting from Only Sparse-View Images
Qijian Tian, Xin Tan, Jingyu Gong +2
We propose a feed-forward Gaussian Splatting model that unifies 3D scene and semantic field reconstruction. Combining 3D scenes with semantic fields facilitates the perception and…
DrivingForward: Feed-forward 3D Gaussian Splatting for Driving Scene Reconstruction from Flexible Surround-view Input
Qijian Tian, Xin Tan, Yuan Xie +1
We propose DrivingForward, a feed-forward Gaussian Splatting model that reconstructs driving scenes from flexible surround-view input. Driving scene images from vehicle-mounted cam…
Generalized Category Discovery in Semantic Segmentation
Zhengyuan Peng, Qijian Tian, Jianqing Xu +5
This paper explores a novel setting called Generalized Category Discovery in Semantic Segmentation (GCDSS), aiming to segment unlabeled images given prior knowledge from a labeled…