Vidi: Large Multimodal Models for Video Understanding and Editing

Vidi Team; Celong Liu; Chia-Wen Kuo; Dawei Du; Fan Chen; Guang Chen; Jiamin Yuan; Lingxi Zhang; Lu Guo; Lusha Li; Longyin Wen; Qingyu Chen; Rachel Deng; Sijie Zhu; Stuart Siew; Tong Jin; Wei Lu; Wen Zhong; Xiaohui Shen; Xin Gu; Xing Mei; Xueqiong Qu; Zhenfang Chen

arXiv · 2504.15681

Vidi: Large Multimodal Models for Video Understanding and Editing

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Abstract

Humans naturally share information with those they are connected to, and video has become one of the dominant mediums for communication and expression on the Internet. To support the creation of high-quality large-scale video content, a modern pipeline requires a comprehensive understanding of both the raw input materials (e.g., the unedited footage captured by cameras) and the editing components (e.g., visual effects). In video editing scenarios, models must process multiple modalities (e.g., vision, audio, text) with strong background knowledge and handle flexible input lengths (e.g., hour-long raw videos), which poses significant challenges for traditional models. In this report, we introduce Vidi, a family of Large Multimodal Models (LMMs) for a wide range of video understand editing scenarios. The first release focuses on temporal retrieval, i.e., identifying the time ranges within the input videos corresponding to a given text query, which plays a critical role in intelligent editing. The model is capable of processing hour-long videos with strong temporal understanding capability, e.g., retrieve time ranges for certain queries. To support a comprehensive evaluation in real-world scenarios, we also present the VUE-TR benchmark, which introduces five key advancements. 1) Video duration: significantly longer than videos of existing temporal retrival datasets, 2) Audio support: includes audio-based queries, 3) Query format: diverse query lengths/formats, 4) Annotation quality: ground-truth time ranges are manually annotated. 5) Evaluation metric: a refined IoU metric to support evaluation over multiple time ranges. Remarkably, Vidi significantly outperforms leading proprietary models, e.g., GPT-4o and Gemini, on the temporal retrieval task, indicating its superiority in video editing scenarios.

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Vidi Team, Celong Liu, Chia-Wen Kuo, Dawei Du, Fan Chen, Guang Chen, Jiamin Yuan, Lingxi Zhang, Lu Guo, Lusha Li, Longyin Wen, Qingyu Chen, Rachel Deng, Sijie Zhu, Stuart Siew, Tong Jin, Wei Lu, Wen Zhong, Xiaohui Shen, Xin Gu, Xing Mei, Xueqiong Qu, Zhenfang Chen. 2025-07-16. Vidi: Large Multimodal Models for Video Understanding and Editing. https://arxiv.org/abs/2504.15681

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