ByteDance Vidi2 Arrives: A Multimodal Model for Precise Video Understanding and Generation

ByteDance Vidi2 Makes a Shocking Debut! Dive into how ByteDance achieves precise understanding and generation of long videos through this Large Multimodal Model. Vidi2 can not only pinpoint specific events like “a man in a brown suit playing drums” but also surpasses Gemini 3 Pro and GPT-5 (Preview) in benchmarks. Explore Vidi2 core technologies and foresee the future of video editing!

This is a scene that resonates deeply with content creators and developers alike: you have a thirty-minute long video on hand, but to find a specific shot—such as “a man in a brown suit playing drums indoors”—you drag back and forth on the timeline, wasting a lot of time. This process of finding a needle in a haystack is often tedious and inefficient.

ByteDance’s Intelligent Creation Team recently released Vidi2, a Large Multimodal Model designed for video understanding and generation. Vidi2 has not only “seen” the video, but it can also understand the details happening in the video and precisely point out the time and location of the event. According to the official report, this model performs even better than well-known models like Gemini 3 Pro (Preview) and GPT-5 on specific benchmarks.

This article will take readers to explore Vidi2’s core technology, new evaluation benchmarks, and how it will change the future of video editing.

What is Vidi2? From Simple Viewing to Precise Positioning

Vidi2 is the second-generation multimodal model launched by ByteDance, focusing on solving two major difficulties in video processing: Video Understanding and Video Creation.

Unlike general visual models, Vidi2 possesses a capability called “Fine-grained Spatio-Temporal Grounding” (STG). This might sound a bit technical, but the principle is actually very intuitive. When you input a text description, Vidi2 can do two things:

1. Temporal Localization: Find the exact time segments (Timestamps) where this description appears in the video.
2. Spatial Localization: Precisely mark the target object with Bounding Boxes in every frame of that time segment.

This means the model not only knows “what happened”, but also “where” and “at which position in the frame” it happened. This end-to-end capability makes complex editing scenes much simpler, such as automatically switching perspectives, understanding plot directions, or intelligent cropping based on picture composition.

Why is this important?

For video editing software, being able to understand screen content is the foundation of automation. The application scenarios demonstrated by Vidi2 include “Smart Split”, which can automatically edit long videos into exciting short clips, recompose to fit mobile vertical screens, and even automatically generate titles and subtitles. This is undoubtedly a great boon for creators who need to process large amounts of footage.

Redefining Standards: VUE-STG and VUE-TR-V2 Benchmarks

To prove Vidi2’s strength, the research team found that existing testing standards were not enough to fully measure the model’s capabilities. Therefore, they introduced two brand new benchmarks, which is also a major highlight of this release.

VUE-STG: Challenging Spatio-Temporal Localization of Long Videos

Existing datasets usually have shorter videos, making it difficult to test the model’s ability to understand long content. VUE-STG made four key improvements regarding this point:

• Large Video Length Span: Covering videos ranging from 10 seconds to 30 minutes, requiring the model to possess long-context and long-time span reasoning capabilities.
• Query Format Optimization: Converting queries into noun phrases while retaining the expressiveness of sentences, closer to human natural search habits.
• High-Quality Annotation: All time ranges and object bounding boxes are precisely annotated manually to ensure the accuracy of test results.
• More Rigorous Evaluation Metrics: Adopting improved vIoU and tIoU mechanisms, optimized for multi-segment spatio-temporal evaluation.

VUE-TR-V2: Upgraded Temporal Retrieval

In addition to spatial localization, the team also upgraded the previous temporal retrieval benchmark and launched VUE-TR-V2. This new version balanced the distribution of video lengths and introduced more “user-style” query sentences. This means the test scenario is closer to how people search for videos in the real world, not just ideal conditions in a lab.

From the data released officially, Vidi2’s performance on these two benchmarks is quite impressive, especially showing extremely high accuracy when handling long videos and complex queries.

Performance Comparison: Rivaling GPT-5 and Gemini

In the technical report, the most eye-catching part is the performance comparison chart. In VUE-STG (Spatio-Temporal Grounding) and VUE-TR-V2 (Temporal Retrieval) tests, Vidi2’s data bars are significantly higher than other competitors.

Specifically, in the VUE-STG test, Vidi2 leads significantly in metrics like tIoU (temporal Intersection over Union) and vIoU (video Intersection over Union). The report specifically compared it with Gemini 3 Pro (Preview) and GPT-5, showing that Vidi2, a specifically optimized model, can surpass general-purpose ultra-large models on specific video understanding tasks.

This actually reflects a trend: although general large models know a bit of everything, specialized models optimized for specific domains (such as fine-grained video spatio-temporal localization) can often provide more precise results. Of course, Vidi2 also achieved competitiveness comparable to open-source models of the same scale on general Video QA benchmarks.

Real Application: Smart Split and Future Outlook

No matter how strong the technology is, it ultimately has to return to application. Vidi2’s technology has already started to show potential in actual tools. The report showed a screenshot of an interface named “TikTok Studio”, where the Smart Split function is a concrete manifestation of Vidi2’s capabilities.

Imagine you uploaded a one-hour travel Vlog, Vidi2 can automatically help you:

1. Identify Highlights: Find the most interesting moments.
2. Reframe: Crop horizontal videos into vertical videos suitable for mobile viewing, while ensuring the protagonist is always in the center of the frame (this requires strong STG capability).
3. Generate Subtitles and Titles: Understand dialogue and context, automatically adding text.

This not only saves editing time but also lowers the threshold for video creation.

Currently, Vidi2’s related code and evaluation scripts have been open-sourced on GitHub, and the official promised “Demo Coming Very Soon”. For developers and researchers, this is an excellent resource for deeply researching multimodal video understanding.

Frequently Asked Questions (FAQ)

Q1: What exactly can Vidi2 do? Vidi2 is a large multimodal model whose main functions include video understanding and generation. Its core feature is “Fine-grained Spatio-Temporal Grounding” (STG), which can precisely find the corresponding time segments in the video based on text instructions and frame the target object in the picture. In addition, it also possesses Video QA and temporal retrieval capabilities.

Q2: How is Vidi2 different from other models (like GPT-4V or Gemini)? Although many models have visual understanding capabilities, Vidi2 specifically strengthened the understanding of “long videos” and the ability of “precise localization”. In the VUE-STG and VUE-TR-V2 benchmarks proposed officially, Vidi2 performed excellently in the accuracy of spatio-temporal localization, even surpassing some general proprietary models on these specific tasks.

Q3: What is Spatio-Temporal Grounding (STG)? STG refers to “Spatio-Temporal Grounding”. Simply put, when you ask the model “Where is a running dog?”, the model can not only tell you “Between 2 minutes 30 seconds and 2 minutes 45 seconds”, but also draw a box on these frames to directly point out the dog’s position. This is the key technology for achieving automated fine editing.

Q4: Where can I use or download Vidi2? ByteDance has currently released related reports, evaluation code, and benchmark datasets (VUE-STG and VUE-TR-V2) on GitHub. The official stated that the Demo is coming soon.

• GitHub Page: https://github.com/bytedance/vidi
• Project Website: https://bytedance.github.io/vidi-website/

Q5: How long of a video does Vidi2 support? According to its proposed benchmark VUE-STG, Vidi2’s design considers long-context reasoning and can handle video content ranging from as short as 10 seconds to as long as about 30 minutes, which is more practical than many models that can only handle short clips.