Identity-Preserving Video Generation Challenge

摘要

Recent advancements in multimodal AIGC have enabled impressive text-to-video synthesis, but a critical challenge remains: maintaining consistent identity of key subjects across generated frames. To address this limitation, we introduce the Identity-Preserving Video Generation (IPVG) grand challenge. This challenge aims to propel the field toward more controllable generative models by focusing community efforts on preserving identity during the video generation process. To support these efforts, we publicly release the Identity-Preserving Video Benchmark (VIP-200K), a novel dataset comprising approximately 500,000 video-prompt pairs with 200,000 unique identities, each coupled with a reference identity image. Through this grand challenge and dataset, we provide a fertile ground for developing solutions that lead to more user-steerable video synthesis systems. The challenge homepage is https://hidream-ai.github.io/ipvg-challenge.github.io/.