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.

通过引入无需微调、编码语言模型驱动的注意力控制框架（LASER），我们提出了一种将语言模型与文本到图像模型相结合的新型框架，用于从单个文本输入创建高质量的动画。

LASER: 无需调整的 LLM 驱动的关注控制以实现高效的文本条件图像转动画

LASER: Tuning-Free LLM-Driven Attention Control for Efficient  Text-conditioned Image-to-Animation

Controllable human motion synthesis is essential for applications in AR/VR,
gaming, movies, and embodied AI. Existing methods often focus solely on either
language or full trajectory control, lacking precision in synthesizing motions
aligned with user-specified trajectories, especially for multi-joint control.
To address these issues, we present TLControl, a new method for realistic human
motion synthesis, incorporating both low-level trajectory and high-level
language semantics controls. Specifically, we first train a VQ-VAE to learn a
compact latent motion space organized by body parts. We then propose a Masked
Trajectories Transformer to make coarse initial predictions of full
trajectories of joints based on the learned latent motion space, with
user-specified partial trajectories and text descriptions as conditioning.
Finally, we introduce an efficient test-time optimization to refine these
coarse predictions for accurate trajectory control. Experiments demonstrate
that TLControl outperforms the state-of-the-art in trajectory accuracy and time
efficiency, making it practical for interactive and high-quality animation
generation.

TLControl 是一种新的方法，结合了低级轨迹和高级语义控制，用于合成真实的人类动作，在轨迹准确性和时间效率方面胜过现有技术，可实用于交互式和高质量的动画生成。