11 citations · 23 across the 10 of their papers we have counts for
7 papers · 1 filter
ImageGem: In-the-wild Generative Image Interaction Dataset for Generative Model Personalization
Yuanhe Guo, Linxi Xie, Zhuoran Chen +5
We introduce ImageGem, a dataset for studying generative models that understand fine-grained individual preferences. We posit that a key challenge hindering the development of such…
Towards Vision-Language-Garment Models for Web Knowledge Garment Understanding and Generation
Jan Ackermann, Kiyohiro Nakayama, Guandao Yang +2
Multimodal foundation models have demonstrated strong generalization, yet their ability to transfer knowledge to specialized domains such as garment generation remains underexplore…
Animal Pose Labeling Using General-Purpose Point Trackers
Zhuoyang Pan, Boxiao Pan, Guandao Yang +2
Automatically estimating animal poses from videos is important for studying animal behaviors. Existing methods do not perform reliably since they are trained on datasets that are n…
Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction
Youming Deng, Wenqi Xian, Guandao Yang +4
In this paper, we present a self-calibrating framework that jointly optimizes camera parameters, lens distortion and 3D Gaussian representations, enabling accurate and efficient sc…
FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models
Tong Wu, Yinghao Xu, Ryan Po +6
Recent advances in text-to-image generation have enabled the creation of high-quality images with diverse applications. However, accurately describing desired visual attributes can…
AIpparel: A Multimodal Foundation Model for Digital Garments
Kiyohiro Nakayama, Jan Ackermann, Timur Levent Kesdogan +6
Apparel is essential to human life, offering protection, mirroring cultural identities, and showcasing personal style. Yet, the creation of garments remains a time-consuming proces…