4 citations · 5 across the 6 of their papers we have counts for
8 papers
PROVE: Training-Free Prompt Recovery using Verifiable Evidence
Rupayan Mallick, Mahsa Khoshnoodi, Sarah Adel Bargal
Modern text-to-image models can generate highly realistic images from natural-language prompts, while recent advances in prompt inversion have made it increasingly feasible to reco…
Gen-AFFECT: Generation of Avatar Fine-grained Facial Expressions with Consistent identiTy
Hao Yu, Rupayan Mallick, Margrit Betke +1
Different forms of customized 2D avatars are widely used in gaming applications, virtual communication, education, and content creation. However, existing approaches often fail to…
GIFT: Gradient-aware Immunization of diffusion models against malicious Fine-Tuning with safe concepts retention
Amro Abdalla, Ismail Shaheen, Dan DeGenaro +3
We present GIFT: a {G}radient-aware {I}mmunization technique to defend diffusion models against malicious {F}ine-{T}uning while preserving their ability to generate safe content. E…
ViSTA: Visual Storytelling using Multi-modal Adapters for Text-to-Image Diffusion Models
Sibo Dong, Ismail Shaheen, Maggie Shen +2
Text-to-image diffusion models have achieved remarkable success, yet generating coherent image sequences for visual storytelling remains challenging. A key challenge is effectively…
D-Feat Occlusions: Diffusion Features for Robustness to Partial Visual Occlusions in Object Recognition
Rupayan Mallick, Sibo Dong, Nataniel Ruiz +1
Applications of diffusion models for visual tasks have been quite noteworthy. This paper targets making classification models more robust to occlusions for the task of object recog…
GenEAva: Generating Cartoon Avatars with Fine-Grained Facial Expressions from Realistic Diffusion-based Faces
Hao Yu, Rupayan Mallick, Margrit Betke +1
Cartoon avatars have been widely used in various applications, including social media, online tutoring, and gaming. However, existing cartoon avatar datasets and generation methods…
FaithFill: Faithful Inpainting for Object Completion Using a Single Reference Image
Rupayan Mallick, Amr Abdalla, Sarah Adel Bargal
We present FaithFill, a diffusion-based inpainting object completion approach for realistic generation of missing object parts. Typically, multiple reference images are needed to a…
Lasagna: Layered Score Distillation for Disentangled Object Relighting
Dina Bashkirova, Arijit Ray, Rupayan Mallick +4
Professional artists, photographers, and other visual content creators use object relighting to establish their photo's desired effect. Unfortunately, manual tools that allow relig…