4 papers
TraceNet: Segment one thing efficiently
Mingyuan Wu, Zichuan Liu, Haozhen Zheng +4
Efficient single instance segmentation is essential for unlocking features in the mobile imaging applications, such as capture or editing. Existing on-the-fly mobile imaging applic…
InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity
Liming Jiang, Qing Yan, Yumin Jia +3
Achieving flexible and high-fidelity identity-preserved image generation remains formidable, particularly with advanced Diffusion Transformers (DiTs) like FLUX. We introduce Infini…
Learning Joint ID-Textual Representation for ID-Preserving Image Synthesis
Zichuan Liu, Liming Jiang, Qing Yan +3
We propose a novel framework for ID-preserving generation using a multi-modal encoding strategy rather than injecting identity features via adapters into pre-trained models. Our me…
Flux Already Knows -- Activating Subject-Driven Image Generation without Training
Hao Kang, Stathi Fotiadis, Liming Jiang +5
We propose a simple yet effective zero-shot framework for subject-driven image generation using a vanilla Flux model. By framing the task as grid-based image completion and simply…