activity
20232026
most citedIDRNet: Intervention-Driven Relation Network for Semantic Segmentation

14 citations · 21 across the 13 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.CV2025

Large Material Gaussian Model for Relightable 3D Generation

Jingrui Ye, Lingting Zhu, Runze Zhang +5

The increasing demand for 3D assets across various industries necessitates efficient and automated methods for 3D content creation. Leveraging 3D Gaussian Splatting, recent large r…

cs.CV2025

AssetDropper: Asset Extraction via Diffusion Models with Reward-Driven Optimization

Lanjiong Li, Guanhua Zhao, Lingting Zhu +4

Recent research on generative models has primarily focused on creating product-ready visual outputs; however, designers often favor access to standardized asset libraries, a domain…

cs.CV2025

StyleAR: Customizing Multimodal Autoregressive Model for Style-Aligned Text-to-Image Generation

Yi Wu, Lingting Zhu, Shengju Qian +4

In the current research landscape, multimodal autoregressive (AR) models have shown exceptional capabilities across various domains, including visual understanding and generation.…

cs.CV2025

MuMA: 3D PBR Texturing via Multi-Channel Multi-View Generation and Agentic Post-Processing

Lingting Zhu, Jingrui Ye, Runze Zhang +8

Current methods for 3D generation still fall short in physically based rendering (PBR) texturing, primarily due to limited data and challenges in modeling multi-channel materials.…

cs.CV2025

Proxy-Tuning: Tailoring Multimodal Autoregressive Models for Subject-Driven Image Generation

Yi Wu, Shengju Qian, Lingting Zhu +5

Multimodal autoregressive (AR) models, based on next-token prediction and transformer architecture, have demonstrated remarkable capabilities in various multimodal tasks including…

cs.CV2025

Large Images are Gaussians: High-Quality Large Image Representation with Levels of 2D Gaussian Splatting

Lingting Zhu, Guying Lin, Jinnan Chen +4

While Implicit Neural Representations (INRs) have demonstrated significant success in image representation, they are often hindered by large training memory and slow decoding speed…