LASER: Tuning-Free LLM-Driven Attention Control for Efficient Text-conditioned Image-to-Animation
<|reference_start|>LASER: Tuning-Free LLM-Driven Attention Control for Efficient Text-conditioned Image-to-Animation: Revolutionary advancements in text-to-image models have unlocked new dimensions for sophisticated content creation, e.g., text-conditioned image editing, allowing us to edit the diverse images that convey highly complex visual concepts according to the textual guidance. Despite being promising, existing methods focus on texture- or non-rigid-based visual manipulation, which struggles to produce the fine-grained animation of smooth text-conditioned image morphing without fine-tuning, i.e., due to their highly unstructured latent space. In this paper, we introduce a tuning-free LLM-driven attention control framework, encapsulated by the progressive process of LLM planning, prompt-Aware editing, StablE animation geneRation, abbreviated as LASER. LASER employs a large language model (LLM) to refine coarse descriptions into detailed prompts, guiding pre-trained text-to-image models for subsequent image generation. We manipulate the model's spatial features and self-attention mechanisms to maintain animation integrity and enable seamless morphing directly from text prompts, eliminating the need for additional fine-tuning or annotations. Our meticulous control over spatial features and self-attention ensures structural consistency in the images. This paper presents a novel framework integrating LLMs with text-to-image models to create high-quality animations from a single text input. We also propose a Text-conditioned Image-to-Animation Benchmark to validate the effectiveness and efficacy of LASER. Extensive experiments demonstrate that LASER produces impressive, consistent, and efficient results in animation generation, positioning it as a powerful tool for advanced digital content creation.<|reference_end|>
@article{zheng2024laser:,
    title={LASER: Tuning-Free LLM-Driven Attention Control for Efficient
  Text-conditioned Image-to-Animation},
    author={Haoyu Zheng, Wenqiao Zhang, Yaoke Wang, Hao Zhou, Jiang Liu, Juncheng
  Li, Zheqi Lv, Siliang Tang, Yueting Zhuang},
    journal={arXiv preprint arXiv:2404.13558},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13558},
    primaryClass={cs.CV}
}