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In this talk, I’ll share practical lessons from building real agent systems in computer vision workflows, focusing on how to design evaluation loops, observability pipelines, and sandboxed environments that make agents reliable in practice. We’ll explore how to measure behavior end-to-end, test components independently, and build feedback loops that help agents improve over time, even as tools, models, and pipelines evolve. This talk is for engineers and builders who want to move beyond demos and learn how to make agent systems production-ready.

The domain of automatic video trailer generation is currently undergoing a profound paradigm shift, transitioning from heuristicbased extraction methods to deep generative synthesis. While early methodologies relied heavily on low-level feature engineering, visual saliency, and rule-based heuristics to select representative shots, recent advancements in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), and diffusion-based video synthesis have enabled systems that not only identify key moments but also construct coherent, emotionally resonant narratives. This survey provides a comprehensive technical review of this evolution, with a specific focus on generative techniques including autoregressive Transformers, LLM-orchestrated pipelines, and text-to-video foundation models like OpenAI's Sora and Google's Veo. We analyze the architectural progression from Graph Convolutional Networks (GCNs) to Trailer Generation Transformers (TGT), evaluate the economic implications of automated content velocity on User-Generated Content (UGC) platforms, and discuss the ethical challenges posed by high-fidelity neural synthesis. By synthesizing insights from recent literature, this report establishes a new taxonomy for AI-driven trailer generation in the era of foundation models, suggesting that future promotional video systems will move beyond extractive selection toward controllable generative editing and semantic reconstruction of trailers.

I will be presenting our recent work, Object-WIPER, which focuses on removing objects and their associated effects from videos. Instead of fine-tuning models for each editing task, our method reuses the priors of pre-trained text-to-video models to perform object and effect removal in a training-free manner. We also curate a real world associated-effect benchmark and evaluation metric for more realistic assessment of video object removal.