AdSpark: A Large-Scale Dataset and Benchmark for Product-Centric Advertisement Video Generation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Product-centric advertisement video generation aims to create promotional videos that preserve fine-grained product identity while presenting selling points through coherent multi-shot narratives. How...
The paper introduces GPUB, a large-scale benchmark for grounded product understanding in e‑commerce livestream videos, featuring 3,000 livestreams, 31K fashion products, and multi‑moment temporal annotations. It defines three evaluation tasks, with the main task (GPrU) requiring simultaneous product identification and moment localization. Existing multimodal models perform poorly on GPrU, prompting the authors to develop UniPro, which improves performance by learning product‑aligned, temporally structured representations.
arXiv:2610.33377v1 Announce Type: cross Abstract: Advertisers routinely create multiple duration variants of ads to meet marketing budgets and viewer preferences, a labor-intensive and costly process...
TLive-Omni is an omni‑modal understanding model designed for e‑commerce live streaming, integrating image, video, audio, and text inputs into a unified representation. It introduces Per‑vGrid for timestamped token organization, a three‑stage supervised training pipeline, and a Faithful‑RFT reinforcement fine‑tuning stage to enhance answer faithfulness and expression quality. The model is supported by a scenario‑oriented capability taxonomy and a compact data production engine that generates training signals for tasks such as speech recognition, product visual grounding, and omni‑modal QA, achieving strong performance on live‑commerce benchmarks and good generalization to general tasks.
arXiv:2606. 19627v1 Announce Type: cross Abstract: The digital commerce landscape is shifting from static, search-driven catalogs to dynamic, immersive video feeds.
The paper introduces AdsCVR, a benchmark for e‑commerce cross‑video reasoning with 2,483 videos and 6,110 QA pairs across six reasoning dimensions. It proposes AdSeek, an agentic framework that actively selects visual and audio tools during multi‑turn exploration and uses an offline trajectory rectification mechanism to improve reinforcement learning. AdSeek achieves 74.30% accuracy on AdsCVR, outperforming its backbone by 27.90 percentage points and generalizes to the open‑domain CrossVid benchmark.