Erasing Unsafe Image and Text Concepts in Image-to-Video Generation

Video generation models have been rapidly improving, but they also amplify the risk of propagating undesired concepts (e.g., unsafe or copyrighted content), motivating reliable concept erasing. Prior video concept erasing largely assumes text-only conditioning, overlooking Image-to-Video settings where the input image provides strong evidence that persists across frames. As a result, existing text-focused erasing methods become ineffective in I2V generation, where the input image may preserve the undesired concept across frames. To address this gap, we propose Image-to-Video Concept Erasing (I2V-CE), a new task that removes an unsafe concept in I2V generation while preserving non-target content and temporal coherence. To achieve this, we introduce UNsafe Image and Text Erasure (UNITE), a training-free method that jointly handles unsafe evidence from image and text conditions: First-frame Masking and Blending (FMB) regenerates concept-related regions in the first-frame latent to suppress unsafe visual evidence, and Token-wise Safe Projection (TSP) projects concept-related token embeddings onto a safe subspace. Extensive experiments show that UNITE enables reliable concept removal in I2V generation across diverse scenarios.