This workshop explores the integration of Artificial Intelligence (AI) into filmmaking, focusing on AI-driven video content analysis (VCA), AI-assisted content creation, and ethical considerations.As AI continues to reshape creative workflows, it opens new possibilities for filmmaking while raising important questions about human-AI collaboration.The workshop aims to bridge perspectives between creative practitioners, industry professionals, and AI researchers, fostering interdisciplinary dialogue on AI's evolving role in creative practice.We will discuss how AI-powered VCA advances film grammar analysis and audience cognition research, informing creative decision-making, and how generative AI supports production processes while expanding artistic possibilities.Additionally, we will examine how AI-driven analysis can inform ethical practices, alongside addressing the risks associated with generative AI.Through a combination of theoretical discussion and practical demonstrations, participants will gain hands-on experience with AI filmmaking tools while critically engaging with the future directions of AI-augmented creativity.

This Paper introduces a novel Enhanced Visual Retrieval and Analysis for Charging Efficiency (EVRACE) system, which presents a novel two-stage framework for visual retrieval and advanced analytics for massive charging curves of electric vehicles. The EVRACE is becoming an important system for enhancing battery performance management, detecting and mitigating charging anomalies, and informing strategic decision-making for stakeholders in the EV ecosystem. EVRACE utilizes deep time-series clustering to categorize charging curves with distinct characteristics and retrieves the most relevant cluster and the top k curves for comprehensive data analysis and risk assessment. This innovative framework substantially enhances retrieval efficiency, mitigates computational complexity, and offers a robust solution for real-time processing of large-scale charging data. Validation using real-world datasets demonstrates that the EVRACE system significantly improves retrieval efficiency and achieves high accuracy in anomaly detection compared to traditional methods.

Mobile autonomy relies on the precise perception of dynamic environments. Robustly tracking moving objects in 3D world thus plays a pivotal role for applications like trajectory prediction, obstacle avoidance, and path planning. While most current methods utilize LiDARs or cameras for Multiple Object Tracking (MOT), the capabilities of 4D imaging radars remain largely unexplored. Recognizing the challenges posed by radar noise and point sparsity in 4D radar data, we introduce RaTrack, an innovative solution tailored for radar-based tracking. Bypassing the typical reliance on specific object types and 3D bounding boxes, our method focuses on motion segmentation and clustering, enriched by a motion estimation module. Evaluated on the View-of-Delft dataset, RaTrack showcases superior tracking precision of moving objects, largely surpassing the performance of the state of the art. We release our code and model at https://github.com/LJacksonPan/RaTrack.