3 papers
cs.CV2026
To Blend In, First Decouple: Rethinking Camouflage Image Generation via Context-Decoupled Representations
Wenzhuang Wang, Yifan Zhao, Mingcan Ma +4
Camouflage image generation (CIG) focuses on generating visually concealed objects that seamlessly blend into their backgrounds. Existing methods typically follow either background…
cs.CV2025
FICGen: Frequency-Inspired Contextual Disentanglement for Layout-driven Degraded Image Generation
Wenzhuang Wang, Yifan Zhao, Mingcan Ma +4
Layout-to-image (L2I) generation has exhibited promising results in natural domains, but suffers from limited generative fidelity and weak alignment with user-provided layouts when…
cs.CV2025
Challenger: Affordable Adversarial Driving Video Generation
Zhiyuan Xu, Bohan Li, Huan-ang Gao +7
Generating photorealistic driving videos has seen significant progress recently, but current methods largely focus on ordinary, non-adversarial scenarios. Meanwhile, efforts to gen…