Title: AlignVid: Training-Free Attention Scaling for Semantic Fidelity in Text-Guided Image-to-Video Generation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Video generation, Semantic fidelity, Attention analysis

Abstract: Text-guided image-to-video (TI2V) generation has recently achieved remarkable progress, particularly in maintaining subject consistency and temporal coherence. However, existing methods still struggle to adhere to fine-grained prompt semantics, especially when prompts entail substantial transformations of the input image (e.g., object addition, deletion, or modification), a shortcoming we term **semantic negligence**. In a pilot study, we find that applying a Gaussian blur to the input image improves semantic adherence. Analyzing attention maps, we observe clearer foreground–background separation. From an energy perspective, this corresponds to a lower-entropy attention distribution. Motivated by this, we introduce **AlignVid**, a training-free framework with two components: (i) *Attention Scaling Modulation (ASM)*, which directly reweights attention via lightweight Q/K scaling, and (ii) *Guidance Scheduling (GS)*, which applies ASM selectively across transformer blocks and denoising steps to reduce visual quality degradation. This minimal intervention improves prompt adherence while limiting aesthetic degradation. In addition, we introduce **OmitI2V** to evaluate semantic negligence in TI2V generation, comprising 367 human-annotated samples that span addition, deletion, and modification scenarios. Extensive experiments demonstrate that AlignVid can enhance semantic fidelity. Code and benchmark will be released.