6 citations · 16 across the 20 of their papers we have counts for
7 papers · 1 filter
Loomis Painter: Learning Painting Processes by Reversing Image-to-Video Generation
Markus Pobitzer, Chang Liu, Chenyi Zhuang +3
Step-by-step painting tutorials are used for learning artistic techniques, but existing video resources lack interactivity and personalization. While recent generative models have…
SceneSplat++: A Large Dataset and Comprehensive Benchmark for Language Gaussian Splatting
Mengjiao Ma, Qi Ma, Yue Li +10
3D Gaussian Splatting (3DGS) serves as a highly performant and efficient encoding of scene geometry, appearance, and semantics. Moreover, grounding language in 3D scenes has proven…
Any Image Restoration via Efficient Spatial-Frequency Degradation Adaptation
Bin Ren, Eduard Zamfir, Zongwei Wu +7
Restoring multiple degradations efficiently via just one model has become increasingly significant and impactful, especially with the proliferation of mobile devices. Traditional s…
Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook
Xu Zheng, Ziqiao Weng, Yuanhuiyi Lyu +7
Retrieval-augmented generation (RAG) has emerged as a pivotal technique in artificial intelligence (AI), particularly in enhancing the capabilities of large language models (LLMs)…
Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration
Yawei Li, Bin Ren, Jingyun Liang +5
While vision transformers achieve significant breakthroughs in various image restoration (IR) tasks, it is still challenging to efficiently scale them across multiple types of degr…
SceneSplat: Gaussian Splatting-based Scene Understanding with Vision-Language Pretraining
Yue Li, Qi Ma, Runyi Yang +10
Recognizing arbitrary or previously unseen categories is essential for comprehensive real-world 3D scene understanding. Currently, all existing methods rely on 2D or textual modali…
CityLoc: 6DoF Pose Distributional Localization for Text Descriptions in Large-Scale Scenes with Gaussian Representation
Qi Ma, Runyi Yang, Bin Ren +4
Localizing textual descriptions within large-scale 3D scenes presents inherent ambiguities, such as identifying all traffic lights in a city. Addressing this, we introduce a method…