In recent years, the rapid development of Large Language Models has highlighted the urgent need for large-scale, high-quality, and diverse data. We have launched an LLM data co-creation platform aimed at bringing together a wide range of participants to contribute data. Within six months, the platform has attracted over 10,000 participants who contributed more than 150,000 data entries across more than 200 tasks. An observable user cohort was constructed around the question, "Who is the best data contributor?" along with sub-questions concerning user preferences, task competence, and more. Through a detailed analysis of data contributors, this paper reveals several data collection patterns related to human factors. It reveals that contributors who provide high-quality data often do not meet initial expectations, as their behavior exhibits typical characteristics of the Dunning-Kruger effect. This paper examined the cognitive bias between users' self-assessment and actual abilities, where individuals tend to overestimate their capabilities in certain tasks, leading to a decreased willingness to continue contributing and a consequent waste of human resources. To address this issue, we propose a task reassignment method based on multi-task fine-tuning of small language models (SLMs) to better align user groups with appropriate task types. After the reallocation, we observed a significant increase in user engagement and platform benefits, along with improved overall platform efficiency. The versatility of this method makes it applicable to broader data collection scenarios.

Recently, text-to-motion models have opened new possibilities for creating realistic human motion with greater efficiency and flexibility. However, aligning motion generation with event-level textual descriptions presents unique challenges due to the complex relationship between textual prompts and desired motion outcomes. To address this, we introduce AToM, a framework that enhances the alignment between generated motion and text prompts by leveraging reward from GPT-4Vision. AToM comprises three main stages: Firstly, we construct a dataset MotionPreferthat pairs three types of event-level textual prompts with generated motions, which cover the integrity, temporal relationship and frequency of motion. Secondly, we design a paradigm that utilizes GPT-4Vision for detailed motion annotation, including visual data formatting, task-specific instructions and scoring rules for each sub-task. Finally, we fine-tune an existing text-to-motion model using reinforcement learning guided by this paradigm. Experimental results demonstrate that AToM significantly improves the event-level alignment quality of text-to-motion generation. Project page is available at https://atom-motion.github.io/.

The cover image is based on the Special Issue Paper Sketch2VF: Sketch-based Flow Design with Conditional Generative Adversarial Network by Xie Haoran et al., https://doi.org/10.1002/cav.1889.

Abstract We present an interactive user interface to support sketch‐based fluid design with a perceptual understanding of human sketches. In particular, the proposed system generates a 2D fluid animation from hand‐drawn sketches. The proposed system utilizes a conditional generative adversarial network model to generate stationary velocity fields from a sketch input. The network model is trained with hand‐drawn strokes and corresponding 2D velocity fields. On the basis of the generated velocity field, the system calculates fluid dynamics using a semi‐Lagrangian method. We ran a user study of the proposed system and confirmed that the proposed interface is effective for a 2D fluid design and that the system achieves good results based on user input.