Consumer content platforms and advertisers stand to benefit from generating high-quality video ads at scale, but current video generation systems often produce structurally unreliable or factually incorrect outputs that can degrade the viewer experience. We propose ATVG (Agentic system for factually grounded Travel advertisement Video Generation), an agentic system that generates travel video ads from only a city name. The system plans clips across multiple themes; grounds clips via an LLM-guided iterative image retrieval process that selects an anchor image; expands lightweight concepts into detailed video prompts via meta-prompts; and verifies generation outputs to detect visual artifacts before assembling the final video. Compared to a single-prompt baseline, our agentic approach improves multi-scene composition and factuality. We demonstrate the system through a web app that exposes intermediate artifacts and full agent logs for 20 cities, covering popular and long-tail cities around the world. The video demonstrating the web app can be found at: https://www.youtube.com/watch?v=-yh_jBDmhGo.

Recommending complementary products1 that fulfill a joint need (e.g., phone case for smartphone) are often overlooked by dynamic product advertising (DPA) systems despite their success on e-Commerce websites such as Amazon. Existing works on complementary product recommendation focus on mining frequently co-purchased products but suffer from low accuracy as co-purchased products are not always complements to each other. More recent works rely on human annotators to clean co-purchased product pairs and use to train end-to-end models for complementary product recommendation. However, unlike e-commerce websites, DPA systems usually do not have access to users' complete shopping history, making the identification of co-purchased products challenging. Moreover, depending on the product types, identifying the complements of a given product may require extensive domain knowledge that is not present in a pair of complementary products. In this work, we propose a novel generate-and-retrieval paradigm to make complementary product recommendations and explore the use of LLMs for this task. Specifically, we rely on LLMs to generate queries that describe the complements of an original product. The generated queries are then used to retrieve relevant products from a product index. The retrieved products are expected to be complementary to the original product. We design experiments using the public Amazon ESCI datasets and compare in-context learning with parameter efficient fine-tuning using models from the GPT and Gemini families for complementary product generation. Our evaluation shows that by leveraging the extensive knowledge of LLMs on product relationship, using only a small number of human-annotated examples, pre-trained LLMs with proper prompt outperform LLMs fine-tuned with tens of thousands human-annotated examples.

The quality of the data and annotation upper-bounds the quality of a downstream model. While there exist large text corpora and image-text pairs, high-quality video-text data is much harder to collect. First of all, manual labeling is more time-consuming, as it requires an annotator to watch an entire video. Second, videos have a temporal dimension, consisting of several scenes stacked together, and showing multiple actions. Accordingly, to establish a video dataset with high-quality captions, we propose an automatic approach leveraging multimodal inputs, such as textual video description, subtitles, and individual video frames. Specifically, we curate 3.8M high-resolution videos from the publicly available HD-VILA-100M dataset. We then split them into semantically consistent video clips, and apply multiple cross-modality teacher models to obtain captions for each video. Next, we finetune a retrieval model on a small subset where the best caption of each video is manually selected and then employ the model in the whole dataset to select the best caption as the annotation. In this way, we get 70M videos paired with high-quality text captions. We dub the dataset as Panda-70M. We show the value of the proposed dataset on three downstream tasks: video captioning, video and text retrieval, and text-driven video generation. The models trained on the proposed data score substantially better on the majority of metrics across all the tasks.