PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework

Generating aesthetic posters is more challenging than simple design images: it requires not only precise text rendering but also the seamless integration of abstract artistic content, striking layouts, and overall stylistic harmony. To address this, we propose PosterCraft, a unified framework that abandons prior modular pipelines and rigid, predefined layouts, allowing the model to freely explore coherent, visually compelling compositions. PosterCraft employs a carefully designed, cascaded workflow to optimize the generation of high-aesthetic posters: (i) large-scale text-rendering optimization on our newly introduced Text-Render-2M dataset; (ii) region-aware supervised fine-tuning on HQ-Poster100K; (iii) aesthetic-text-reinforcement learning via best-of-n preference optimization; and (iv) joint vision-language feedback refinement. Each stage is supported by a fully automated data-construction pipeline tailored to its specific needs, enabling robust training without complex architectural modifications. Evaluated on multiple experiments, PosterCraft significantly outperforms open-source baselines in rendering accuracy, layout coherence, and overall visual appeal-approaching the quality of SOTA commercial systems. Our code, models, and datasets can be found in the Project page: https://ephemeral182.github.io/PosterCraft

Active Data Acquisition in Autonomous Driving Simulation

Autonomous driving algorithms rely heavily on learning-based models, which require large datasets for training. However, there is often a large amount of redundant information in these datasets, while collecting and processing these datasets can be time-consuming and expensive. To address this issue, this paper proposes the concept of an active data-collecting strategy. For high-quality data, increasing the collection density can improve the overall quality of the dataset, ultimately achieving similar or even better results than the original dataset with lower labeling costs and smaller dataset sizes. In this paper, we design experiments to verify the quality of the collected dataset and to demonstrate this strategy can significantly reduce labeling costs and dataset size while improving the overall quality of the dataset, leading to better performance of autonomous driving systems. The source code implementing the proposed approach is publicly available on https://github.com/Th1nkMore/carla_dataset_tools.