Video Anomaly Detection (VAD) aims to temporally localize abnormal events in videos. Most existing approaches rely on dataset-specific training and curated annotations, limiting generalization in open-set scenarios. Recent zero-shot methods based on Large Vision- Language Models (LVLMs) alleviate this dependency but of...
Mohd Ubaid Wani, Sara Atito, Josef Kittler et al.· 0 citations
SyncDreamer is presented, a unified diffusion Transformer framework that generates identity-preserving and emotionally expressive talking avatars from only a single image, speech audio, and text prompt, and an RL-based Cross-Modal Prompt Enhancer grounding textual cues in visual context for fine-grained motion con-trol...
Fatemeh Nazarieh, Zhenhua Feng, Diptesh Kanojia et al.· 0 citations
MorphUNet is the first diffusion-based morphing framework using trainable parent-separated dual cross-attention inside the denoising U-Net: a Biometric Transport Layer carrying parent-specific identity evidence through denoising, attending to each parent separately before combining residuals via the morphing parameter...
T. Rizwan, Sara Atito, Zhenhua Feng et al.· arXiv.org· 0 citations
Generative diffusion models have revolutionized facial image synthesis, yet robust identity preservation in high resolution outputs remains a critical challenge. This issue is especially vital for security systems, biometric authentication, and privacy sensitive applications, where any drift in identity integrity can u...
T. Rizwan, Sara Atito, Muhammad Awais et al.· arXiv.org· 0 citations
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