Title: E-CommerceVideo: A Benchmark and approach for E-Commerce Video Generation from product Images
Primary area: datasets and benchmarks
Keywords: Image to video generation, diffusion model, video generation benchmark

Abstract: We introduce the task of E-Commerce Video Generation, which aims to automatically produce dynamic product showcase videos from static product images and a text prompt describing the video's motion and background. Unlike general video generation, this task requires strict adherence to the product's identity and visual features. Even small distortions in color, texture, or logos are not acceptable for commercial use. Existing methods are not directly applicable, as single-image conditioning often leads to hallucinations of product details, while strict frame-based conditioning severely limits motion diversity.
To address this gap, we present E-CommerceVideo, a large-scale, multi-modal dataset curated from the Taobao product repository. This benchmark dataset comprises a massive collection of triplets: product images, corresponding textual descriptions, and high-quality videos. 
We further establish a simple yet efficient baseline method by adapting a pre-trained video generation model with a VAE-based spatial injection mechanism, which preserves product appearance while enabling motion synthesis. Comprehensive evaluation on VBench demonstrates the baseline’s potential as an entry point for subsequent advancements.