3 citations · 6 across the 10 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…
Fixing the Perspective: A Critical Examination of Zero-1-to-3
Jack Yu, Xueying Jia, Charlie Sun +1
Novel view synthesis is a fundamental challenge in image-to-3D generation, requiring the generation of target view images from a set of conditioning images and their relative poses…
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…
EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry Images
Wangbo Yu, Chaoran Feng, Jiye Tang +6
3D Gaussian Splatting (3D-GS) has demonstrated exceptional capabilities in 3D scene reconstruction and novel view synthesis. However, its training heavily depends on high-quality,…
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…
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…