14 citations · 21 across the 13 of their papers we have counts for
7 papers · 1 filter
AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer
Lingting Zhu, Shengju Qian, Haidi Fan +6
The digital industry demands high-quality, diverse modular 3D assets, especially for user-generated content~(UGC). In this work, we introduce AssetFormer, an autoregressive Transfo…
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…
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…
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.…
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.…
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…