AI RESEARCH
Corruption-Aware Training of Latent Video Diffusion Models for Robust Text-to-Video Generation
arXiv CS.LG
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ArXi:2505.21545v3 Announce Type: replace-cross Latent Video Diffusion Models (LVDMs) have achieved state-of-the-art generative quality for image and video generation; however, they remain brittle under noisy conditioning, where small perturbations in text or multimodal embeddings can cascade over timesteps and cause semantic drift. Existing corruption strategies from image diffusion (Gaussian, Uniform) fail in video settings because static noise disrupts temporal fidelity. In this paper, we propose CAT-LVDM, a corruption-aware.