3 papers
cs.CV2026
Pose-ICL: 3D-Aware In-Context Learning for Pose-Controllable Subject Customization
Xuan Han, Yihao Zhao, Mingyu You
Subject Customization is a foundational task in modern image generation. By providing a few reference images and a text prompt, users can generate images of a specific object in an…
cs.CV2026
RaPD: Resolution-Agnostic Pixel Diffusion via Semantics-Enriched Implicit Representations
Yanhao Ge, Shanyan Guan, Weihao Wang +2
Natural images are continuous, yet most generative models synthesize them on discrete grids, limiting resolution-flexible generation. Continuous neural fields enable resolution-fre…
cs.CV2024
HybridBooth: Hybrid Prompt Inversion for Efficient Subject-Driven Generation
Shanyan Guan, Yanhao Ge, Ying Tai +3
Recent advancements in text-to-image diffusion models have shown remarkable creative capabilities with textual prompts, but generating personalized instances based on specific subj…