8 citations · 12 across the 21 of their papers we have counts for
7 papers · 1 filter
ReNeg: Learning Negative Embedding with Reward Guidance
Xiaomin Li, Yixuan Liu, Takashi Isobe +8
In text-to-image (T2I) generation applications, negative embeddings have proven to be a simple yet effective approach for enhancing generation quality. Typically, these negative em…
Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding
Wenbo Zhang, Lu Zhang, Ping Hu +3
Injecting semantics into 3D Gaussian Splatting (3DGS) has recently garnered significant attention. While current approaches typically distill 3D semantic features from 2D foundatio…
DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image Inpainting
Yicheng Yang, Pengxiang Li, Lu Zhang +6
Subject-driven image inpainting has recently gained prominence in image editing with the rapid advancement of diffusion models. Beyond image guidance, recent studies have explored…
CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Models
Qinghe Wang, Baolu Li, Xiaomin Li +4
Recent advances in text-to-image models have opened new frontiers in human-centric generation. However, these models cannot be directly employed to generate images with consistent…
Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases
Kai Chen, Yanze Li, Wenhua Zhang +10
Large Vision-Language Models (LVLMs) have received widespread attention for advancing the interpretable self-driving. Existing evaluations of LVLMs primarily focus on multi-faceted…
PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation
Junsong Chen, Chongjian Ge, Enze Xie +7
In this paper, we introduce PixArt-Σ, a Diffusion Transformer model~(DiT) capable of directly generating images at 4K resolution. PixArt-Σrepresents a significant advancement over…
StableIdentity: Inserting Anybody into Anywhere at First Sight
Qinghe Wang, Xu Jia, Xiaomin Li +4
Recent advances in large pretrained text-to-image models have shown unprecedented capabilities for high-quality human-centric generation, however, customizing face identity is stil…