AlignVid: Taming Visual Dominance via Training-Free Attention Modulation in Text-guided Image-to-Video Generation

Text-guided image-to-video generation has made substantial progress, yet it still struggles to execute text-specified edits that require substantial changes to a reference image (e.g., object addition, deletion, or modification). Empirically, our analysis reveals that this stems from visual dominance , where the reference image causes severe attention dispersion, inhibiting the model's ability to incorporate new semantic information. To address this, we propose AlignVid , a training-free intervention that re-calibrates the model's internal attention distribution. Drawing on an energy-based perspective of attention, AlignVid employs Attention Scaling Modulation ( ASM ) to reduce attention entropy and concentrate focus on semantic tokens, alongside Guidance Scheduling ( GS ) to maintain generation stability. To rigorously assess this capability, we present OmitI2V , a comprehensive benchmark for evaluating prompt adherence across object addition, deletion, and modification. Extensive experiments demonstrate that AlignVid effectively enhances semantic fidelity with negligible computational overhead.