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arXiv · 2604.18326

OmniHuman: A Large-scale Dataset and Benchmark for Human-Centric Video Generation

Lei Zhu·Xing Cai·Yingjie Chen·Yiheng Li·Binxin Yang·Hao Liu·Jie Chen·Chen Li·Jing LYu
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Abstract

Recent advancements in audio-video joint generation models have demonstrated impressive capabilities in content creation. However, generating high-fidelity human-centric videos in complex, real-world physical scenes remains a significant challenge. We identify that the root cause lies in the structural deficiencies of existing datasets across three dimensions: limited global scene and camera diversity, sparse interaction modeling (both person-person and person-object), and insufficient individual attribute alignment. To bridge these gaps, we present OmniHuman, a large-scale, multi-scene dataset designed for fine-grained human modeling. OmniHuman provides a hierarchical annotation covering video-level scenes, frame-level interactions, and individual-level attributes. To facilitate this, we develop a fully automated pipeline for high-quality data collection and multi-modal annotation. Complementary to the dataset, we establish the OmniHuman Benchmark (OHBench), a three-level evaluation system that provides a scientific diagnosis for human-centric audio-video synthesis. Crucially, OHBench introduces metrics that are highly consistent with human perception, filling the gaps in existing benchmarks by providing a comprehensive diagnosis across global scenes, relational interactions, and individual attributes.

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Lei Zhu, Xing Cai, Yingjie Chen, Yiheng Li, Binxin Yang, Hao Liu, Jie Chen, Chen Li, Jing LYu. 2026-04-20. OmniHuman: A Large-scale Dataset and Benchmark for Human-Centric Video Generation. https://arxiv.org/abs/2604.18326

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