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20242026
most citedFastVGGT: Training-Free Acceleration of Visual Geometry Transformer

2 citations · 4 across the 19 of their papers we have counts for

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11 papers · 1 filter

cs.CV2025

FVAR: Visual Autoregressive Modeling via Next Focus Prediction

Xiaofan Li, Chenming Wu, Yanpeng Sun +6

Visual autoregressive models achieve remarkable generation quality through next-scale predictions across multi-scale token pyramids. However, the conventional method uses uniform s…

cs.CV2025★ 2 cited

FastVGGT: Training-Free Acceleration of Visual Geometry Transformer

You Shen, Zhipeng Zhang, Yansong Qu +4

Foundation models for 3D vision have recently demonstrated remarkable capabilities in 3D perception. However, scaling these models to long-sequence image inputs remains a significa…

cs.GR2025

XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding

Dian Chen, Yansong Qu, Xinyang Li +2

Current auto-regressive models can generate high-quality, topologically precise meshes; however, they necessitate thousands-or even tens of thousands-of next-token predictions duri…

cs.CV2025

Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection

Yilin Lu, Jianghang Lin, Linhuang Xie +5

Anomaly inspection plays a vital role in industrial manufacturing, but the scarcity of anomaly samples significantly limits the effectiveness of existing methods in tasks such as l…

cs.CV2025

DeOcc-1-to-3: 3D De-Occlusion from a Single Image via Self-Supervised Multi-View Diffusion

Yansong Qu, Shaohui Dai, Xinyang Li +4

Reconstructing 3D objects from a single image remains challenging, especially under real-world occlusions. While recent diffusion-based view synthesis models can generate consisten…

cs.CV2025

SynergyAmodal: Deocclude Anything with Text Control

Xinyang Li, Chengjie Yi, Jiawei Lai +4

Image deocclusion (or amodal completion) aims to recover the invisible regions (\ie, shape and appearance) of occluded instances in images. Despite recent advances, the scarcity of…

cs.CV2025

Training-Free Hierarchical Scene Understanding for Gaussian Splatting with Superpoint Graphs

Shaohui Dai, Yansong Qu, Zheyan Li +3

Bridging natural language and 3D geometry is a crucial step toward flexible, language-driven scene understanding. While recent advances in 3D Gaussian Splatting (3DGS) have enabled…

cs.CV2025

WildSeg3D: Segment Any 3D Objects in the Wild from 2D Images

Yansong Guo, Jie Hu, Yansong Qu +1

Recent advances in interactive 3D segmentation from 2D images have demonstrated impressive performance. However, current models typically require extensive scene-specific training…

cs.CV2025★ 1 cited

AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesis

Zhangyu Lai, Yilin Lu, Xinyang Li +5

While existing anomaly synthesis methods have made remarkable progress, achieving both realism and diversity in synthesis remains a major obstacle. To address this, we propose Anom…

cs.CV2025

Evolving High-Quality Rendering and Reconstruction in a Unified Framework with Contribution-Adaptive Regularization

You Shen, Zhipeng Zhang, Xinyang Li +4

Representing 3D scenes from multiview images is a core challenge in computer vision and graphics, which requires both precise rendering and accurate reconstruction. Recently, 3D Ga…

cs.GR2025

Drag Your Gaussian: Effective Drag-Based Editing with Score Distillation for 3D Gaussian Splatting

Yansong Qu, Dian Chen, Xinyang Li +4

Recent advancements in 3D scene editing have been propelled by the rapid development of generative models. Existing methods typically utilize generative models to perform text-guid…