Vidi2: Revolutionizing Video Understanding and Creation with Precision Spatial-Temporal AI

ByteDance’s Next-Generation Multimodal Model Outperforms Industry Leaders in Video Grounding and Retrieval

Video has become the dominant language of the internet. From short-form content that captures our attention in seconds to long-form storytelling that keeps us engaged for hours, video is how we communicate, learn, and express creativity. Yet behind every compelling video lies hours of painstaking work—searching through footage, tracking objects frame by frame, and understanding complex narratives. What if AI could not only watch videos but truly understand them with the precision of a professional editor?

Enter Vidi2, the second-generation large multimodal model from ByteDance’s Intelligent Creation team. Building on the success of its predecessor, Vidi2 doesn’t just find relevant clips; it pinpoints exactly where objects appear, tracks their movement through time and space, and comprehends the underlying story. In this comprehensive guide, we’ll explore how Vidi2 is reshaping video AI, from its groundbreaking capabilities to practical implementation.

What Makes Vidi2 Different: Three Core Capabilities That Matter

Vidi2 isn’t another generic video analysis tool. It’s a specialized foundation model designed for fine-grained video understanding. Let’s break down what that means in practice.

1. Spatio-Temporal Grounding: Finding Objects in Both Time and Space

Imagine you’re editing a documentary and need to locate “the red car that appears during the interview segment.” Traditional video search might point you to the right timestamp. Vidi2 goes further—it draws a precise bounding box around that car in every frame, creating what’s called a spatio-temporal tube.

This capability solves a fundamental problem in video editing: temporal retrieval tells you when something happens; spatio-temporal grounding tells you where it happens throughout the entire duration.

For example, when given the query “a man standing up from a kneeling position”, Vidi2 produces:

[
  {"timestamp":"00:01:01","box_2d":[0.462,0.391,0.537,0.657]},
  {"timestamp":"00:01:02","box_2d":[0.494,0.375,0.557,0.659]},
  {"timestamp":"00:01:03","box_2d":[0.494,0.281,0.569,0.660]}
]

Each entry represents one second of the video, with normalized coordinates pinpointing the man’s location. This isn’t just convenient—it’s transformative for applications like automatic multi-camera switching, where you need to know exactly where each subject is to cut between angles seamlessly.

2. Temporal Retrieval: Mastering Long-Form Video Navigation

While Vidi1 pioneered temporal retrieval, Vidi2 takes it to another level, especially for long-context understanding. The model can process videos ranging from 10 seconds to 30 minutes (and even longer in practice), maintaining consistent performance across durations.

The key improvement? Robustness in ultra-long videos. When benchmarked against videos exceeding 60 minutes, Vidi2 maintained a temporal IoU (Intersection over Union) of 38.65%, while competitors like GPT-5 dropped to 12.49%. This matters because real-world content—podcasts, lectures, live streams—often runs for hours.

Whether you’re searching for a “basketball statue” in a 46-second clip or tracking mentions of “North Devon Marine Pioneer” across an 84-minute documentary, Vidi2 delivers precise time ranges without breaking a sweat.

3. Video Question Answering: Bridging Retrieval and Reasoning

Vidi2 extends beyond retrieval into general video understanding. The model can answer multiple-choice questions about video content, demonstrating solid multimodal reasoning capabilities.

On established benchmarks like LVBench, LongVideoBench, and VideoMME, Vidi2 achieves competitive performance comparable to leading open-source models such as Qwen2.5-VL-7B. While it doesn’t yet match specialized QA models like Gemini 2.5 Pro, this capability ensures Vidi2 isn’t just a narrow tool—it’s a versatile foundation model ready for diverse video understanding tasks.

Under the Hood: How Vidi2 Works

Understanding Vidi2’s architecture helps clarify why it outperforms alternatives. The technical design reflects a deliberate focus on video-specific challenges.

Unified Multimodal Architecture

At its core, Vidi2 processes text, visual, and audio inputs through a single unified pipeline:

  • Visual Encoding: Videos are sampled at 1 frame per second, with each frame processed through a vision encoder. A single image is treated as a 1-second silent video, creating a consistent interface for both image and video inputs.
  • Adaptive Token Compression: Long videos pose a challenge—too many frames overwhelm the model; too few miss details. Vidi2 employs a redesigned compression strategy that balances efficiency and fidelity across different video lengths, ensuring short clips retain fine details while long videos don’t exceed computational limits.
  • LLM Backbone: The 12-billion parameter model (12B) uses Gemma-3 as its language foundation, enabling sophisticated reasoning about visual and temporal relationships.

This architecture allows Vidi2 to genuinely synchronize multimodal information. When a query mentions “the alarm sound,” the model doesn’t just search for the word—it correlates the audio event with subsequent visual reactions.

Training Data Strategy: Quality Over Quantity

Vidi2’s training follows a three-stage pipeline with a critical insight: increasing real video data proportion dramatically improves performance.

For the new spatio-temporal grounding capability, the team employed a clever two-step approach:

Synthetic Data Bridging: Since large-scale video spatio-temporal annotations don’t exist, the researchers first leveraged existing image-level grounding datasets (like COCO and RefCOCO). By treating these as “frozen frames” from hypothetical videos, they synthesized massive amounts of video grounding pairs. This bridges the gap between static image understanding and dynamic video analysis.

Real-World Refinement: The team then manually annotated thousands of real videos with precise temporal and spatial labels. The VUE-STG benchmark itself serves as both evaluation metric and training data source—a virtuous cycle that ensures the model learns from realistic scenarios.

Transfer Learning from Retrieval to Grounding

A fascinating technical observation emerged during development: temporal-aware alignment training for retrieval generalizes remarkably well to video QA tasks. The time-understanding capabilities that made Vidi1 excel at finding clips also help Vidi2 reason about causality and sequence in question-answering scenarios. This cross-task synergy means improvements in one area automatically benefit others.

Performance Deep Dive: Numbers That Matter

Benchmarks can be misleading if they don’t reflect real use cases. Vidi2’s evaluation framework, built around two new benchmarks, demonstrates performance where it counts.

VUE-STG: A Benchmark for the Real World

Existing academic datasets for spatio-temporal grounding have critical flaws: they’re too short (most under 30 seconds), use oversimplified queries, and rely on noisy automatic annotations. VUE-STG (Video Understanding Evaluation – Spatio-Temporal Grounding) addresses these gaps with four key improvements:

1. Video Duration That Reflects Reality

Duration Category Video Count Total Hours
Ultra-short (<1 min) 126 0.82
Short (1-10 min) 294 26.28
Medium (10-30 min) 562 177.69
Total 982 204.79

With videos up to 30 minutes, VUE-STG tests long-context reasoning—a must-have for editing webinars, interviews, and documentary footage.

2. Query Format Optimized for Clarity

Instead of full sentences that can be ambiguous, queries are reformulated into noun phrases while preserving expressiveness. For example, “a player is loaded into an ambulance” becomes “the ambulance which the player is being loaded into” when the ambulance is the target, or “the player who is loaded into the ambulance” when tracking the person. This subtle change eliminates semantic ambiguity and improves annotation reliability.

3. Manual Annotation at Scale

Every single timestamp and bounding box in VUE-STG is manually labeled with high precision. The result:

  • 1,600 queries across 982 videos
  • 12,147 bounding boxes annotated at 1 frame per second
  • Consistent quality even for objects that temporarily leave the frame

4. Evaluation Metrics That Capture Complexity

Traditional IoU (Intersection over Union) fails for multi-segment scenarios. VUE-STG introduces a refined metric suite:

  • tIoU/tP/tR: Temporal precision, recall, and IoU measuring time range accuracy
  • vIoU/vP/vR: Spatio-temporal metrics that average bounding box IoU across overlapping frames
  • vIoU-Intersection: Calculated only where predictions and ground truth overlap, isolating spatial accuracy from temporal coverage

A spatio-temporal tube is modeled as a time-dependent bounding box B(t) = (x₀(t), y₀(t), x₁(t), y₁(t)). The vIoU metric averages frame-level IoU across the temporal union of predicted and ground-truth tubes, providing a single number that captures both spatial and temporal alignment quality.

Head-to-Head Results: Vidi2 vs. Industry Leaders

The numbers speak for themselves. On VUE-STG, Vidi2 demonstrates significant margins across all categories:

Overall Performance

Model vIoU vIoU-Int. tIoU
Vidi2 32.57% 60.30% 53.19%
Gemini 3 Pro (Preview) 4.61% 16.59% 27.50%
GPT-5 5.47% 33.64% 16.40%
Qwen3-VL-32B 5.12% 18.47% 25.91%

The gap is most dramatic in challenging scenarios:

Medium-Length Videos (10-30 minutes): Vidi2 achieves 28.18% vIoU while competitors fall below 3.1%. This is crucial for real editors working with interview footage, event recordings, and educational content.

Small Objects (<10% of frame): Vidi2 maintains 23.31% vIoU compared to Gemini’s 2.33% and GPT-5’s 3.66%. For applications like surveillance or sports analysis where targets are often distant, this is a game-changer.

Temporal Continuity Handling: When objects disappear due to occlusion or camera cuts, Vidi2’s vIoU-Intersection of 60.30% shows it can maintain accurate spatial tracking once the target reappears—a common failure point for other models.

VUE-TR-V2: Upgraded Temporal Retrieval Benchmark

The original VUE-TR benchmark was updated to VUE-TR-V2 with two major improvements:

  • More balanced duration distribution: Ultra-long videos (>60 min) increased from 15 to 39, reducing bias toward short clips
  • Total duration: 310.72 hours (vs. 107.87 in V1), an increase of 188%

On this more challenging benchmark, Vidi2 again leads significantly:

Video Category Vidi2 tIoU Gemini 3 Pro tIoU GPT-5 tIoU
Overall 48.75% 37.58% 17.15%
Ultra-short (<1 min) 59.09% 56.72% 41.08%
Medium (10-30 min) 51.05% 40.56% 11.28%
Long (30-60 min) 48.15% 26.20% 9.39%
Ultra-long (>60 min) 38.65% 21.19% 12.49%

The ROC curves across precision, recall, and IoU thresholds show Vidi2 consistently outperforming competitors at every operating point. This means whether you need high-confidence predictions for automatic editing or broader recall for manual review, Vidi2 adapts to your requirements.

Video QA: Solid Foundation for General Understanding

While not Vidi2’s primary focus, its video QA performance is respectable:

  • LVBench: 45.8% (vs. Qwen2.5-VL-7B’s 45.3%)
  • LongVideoBench: 57.1% (beating Qwen2.5-VL-7B’s 54.7%)
  • VideoMME: 63.5% (slightly behind Qwen2.5-VL-7B’s 65.1%)

These results confirm that Vidi2 is a well-rounded foundation model suitable for multimodal reasoning tasks beyond pure retrieval and grounding.

Real-World Applications: From Research to Production

Vidi2’s capabilities aren’t theoretical. They’re already powering next-generation video creation tools at ByteDance and beyond.

Application 1: AI-Powered Highlight Extraction

Content creators face a constant challenge: distilling hours of raw footage into engaging short clips. Vidi2’s temporal retrieval capability automates this process with remarkable precision.

Upload a vlog, and Vidi2 identifies moments like:

  • “When You’re Trying to Be Quiet But…” (00:32-00:54)
  • “This Bunny’s Morning Routine is… Different” (02:10-02:45)

Each highlight comes with a contextual title that captures the essence of the segment. The model understands not just what’s happening visually but the narrative significance of each moment. This feature is already available in TikTok’s Smart Split tool, helping creators produce publish-ready content without manual scrubbing.

Application 2: Plot-Level Understanding for Narrative Editing

Editing movies or TV series requires understanding complex character relationships and causal chains. Traditional tools are blind to story structure. Vidi2 changes this.

Consider a detective scene with multiple suspects. A human editor might ask: “Show me all scenes where the dentist reveals her financial crimes.” Vidi2 can parse this high-level query by:

  1. Recognizing characters across shots using spatio-temporal tubes
  2. Understanding dialogue and visual context to identify the “dentist” character
  3. Reasoning about motivations from script and action cues
  4. Retrieving relevant intervals where the plot point occurs

The technical report shows an example where Vidi2 correctly answers: “The dentist practices tax evasion and hires Bernice because she cannot report the theft without exposing her own crimes.” This level of causal reasoning enables automatic generation of character-specific cuts or thematic montages.

Application 3: Storyline-Driven Video Creation

Perhaps the most ambitious application is end-to-end video creation from multiple assets. Given six unrelated video clips, Vidi2 can:

  1. Analyze each clip’s content through spatio-temporal grounding
  2. Identify a unifying narrative thread (e.g., “Best Friend’s Observation Journal”)
  3. Structure an emotional arc with clear setup, conflict, and resolution
  4. Generate a complete editing script including:

    • Precise shot timestamps (e.g., Asset 3, 00:15-00:19)
    • Voiceover scripts
    • Subtitle styling instructions
    • Speed adjustments and transition effects
  5. Produce a publish-ready video with narration, music, and graphics

This moves AI from tool to creative partner, capable of understanding both the technical and artistic aspects of video production.

Getting Started: Installation and Implementation Guide

Ready to experiment with Vidi2? Here’s a complete walkthrough based on the official release.

System Requirements

  • Operating System: Linux (Ubuntu 20.04+) or macOS (12.0+)
  • Python: 3.8 or higher
  • GPU: NVIDIA GPU with at least 16GB VRAM (24GB recommended for 12B model)
  • Storage: 50GB for model weights, additional space for video processing

Step 1: Environment Setup

# Clone the repository (when officially released)
git clone https://github.com/bytedance/vidi.git
cd vidi

# Run automated installation
bash install.sh

The install.sh script handles:

  • PyTorch installation with CUDA support
  • Video processing libraries (FFmpeg, Decord)
  • Model dependencies and Python packages
  • Environment validation

Step 2: Model Weight Download

Currently, Vidi-7B weights are available. The full Vidi2-12B weights will be released soon.

# Method 1: Hugging Face CLI (Recommended)
git lfs install
git clone https://huggingface.co/bytedance-research/Vidi-7B ./models/vidi-7b

# Method 2: Manual Download
# Visit https://huggingface.co/bytedance-research/Vidi-7B
# Download all .bin files and config.json to ./models/vidi-7b

File structure:

./models/vidi-7b/
├── config.json
├── model-00001-of-00008.bin
├── model-00002-of-00008.bin
├── ...
├── tokenizer.json
└── processor_config.json

Step 3: Basic Inference

Run your first spatio-temporal grounding prediction:

cd Vidi_7B  # Or future Vidi2 directory

python3 -u inference.py \
    --video-path /path/to/your/video.mp4 \
    --query "A woman in a blue jacket walking through the park" \
    --model-path ./models/vidi-7b \
    --output-format json \
    --confidence-threshold 0.7

Parameters explained:

  • --video-path: Supports MP4, AVI, MOV, MKV formats
  • --query: Natural language description. English yields best results; other languages may work but are not benchmarked
  • --model-path: Directory containing model weights
  • --output-format: JSON for structured data, CSV for spreadsheet analysis
  • --confidence-threshold: Filters low-confidence predictions (0.0-1.0)

Expected output:

{
  "video_duration": 612.5,
  "query": "A woman in a blue jacket walking through the park",
  "tubes": [
    {"timestamp": 45, "bbox": [0.234, 0.156, 0.456, 0.789], "confidence": 0.85},
    {"timestamp": 46, "bbox": [0.245, 0.167, 0.467, 0.801], "confidence": 0.87},
    {"timestamp": 47, "bbox": [0.256, 0.178, 0.478, 0.813], "confidence": 0.83}
  ],
  "aggregate_metrics": {
    "temporal_coverage": 23.5,
    "mean_confidence": 0.85
  }
}

Performance benchmarks (on NVIDIA A100):

  • 1-minute video: ~30 seconds processing time
  • 10-minute video: ~3-5 minutes
  • 30-minute video: ~8-12 minutes
  • VRAM usage: ~18GB for 10-minute video at 1FPS

Step 4: Visualizing Results

Convert the JSON output to a visual overlay:

import cv2
import json

# Load results
with open('output.json', 'r') as f:
    data = json.load(f)

# Open video
cap = cv2.VideoCapture('your_video.mp4')
fps = cap.get(cv2.CAP_PROP_FPS)

for tube in data['tubes']:
    frame_num = int(tube['timestamp'] * fps)
    cap.set(cv2.CAP_PROP_POS_FRAMES, frame_num)
    
    ret, frame = cap.read()
    if ret:
        # Convert normalized coords to pixel values
        h, w = frame.shape[:2]
        x0, y0, x1, y1 = [int(c * dim) for c, dim in zip(tube['bbox'], [w, h, w, h])]
        
        # Draw bounding box
        cv2.rectangle(frame, (x0, y0), (x1, y1), (0, 255, 0), 2)
        cv2.putText(frame, f"TS: {tube['timestamp']}", (x0, y0-10), 
                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
        
        cv2.imwrite(f'frame_{tube["timestamp"]}.png', frame)

cap.release()

Step 5: Running Official Evaluations

To benchmark your own model or reproduce Vidi2 results:

VUE-STG Evaluation

cd VUE_STG

# Step 1: Download benchmark videos
# WARNING: 200+ hours of content (~500GB)
python3 scripts/download_videos.py --video_list vue-stg-benchmark/video.csv

# Step 2: Generate predictions in required format
# See format specification in docs/evaluation_format.md

# Step 3: Run evaluation
python3 evaluate.py \
    --pred_path results/your_model/tubes.csv \
    --gt_path vue-stg-benchmark/gt_tubes.csv \
    --output_dir ./results/your_model

# View detailed metrics
cat results/your_model/metrics_detailed.json

Evaluation output includes:

  • Temporal metrics (tP, tR, tIoU) overall and per-duration bucket
  • Spatio-temporal metrics (vP, vR, vIoU, vIoU-Int.)
  • Breakdown by object size (small, medium, large)
  • Per-query analysis for failure case inspection

VUE-TR-V2 Evaluation

cd VUE_TR_V2

# Run evaluation on temporal retrieval predictions
python3 -u qa_eval.py \
    --pred_path results/your_model_predictions.json \
    --gt_path ground_truth/ \
    --output_dir ./results/your_model

# Generate visualization plots
python3 scripts/plot_metrics.py --results_dir ./results/your_model

Result formats:

  • Radar charts comparing IoU across duration categories
  • ROC-style curves for precision/recall thresholds
  • JSON file with all raw metrics for custom analysis

Frequently Asked Questions

Based on community feedback, here are the most common questions about Vidi2:

Q1: How does Vidi2 differ from the original Vidi model?

Vidi1 focused exclusively on temporal retrieval—finding when things happen. Vidi2 introduces end-to-end spatio-temporal grounding, simultaneously returning timestamps and bounding boxes. The architecture has been upgraded to Gemma-3 backbone, training data now includes 3x more real videos, and the evaluation suite (VUE-STG/VUE-TR-V2) is entirely new. Think of Vidi1 as a smart video search engine; Vidi2 is a video understanding co-pilot.

Q2: Why does Vidi2 excel at long videos and small objects?

Three design decisions make this possible:

  1. Dense 1FPS sampling: Captures more temporal detail than sparse keyframe approaches
  2. Spatial-temporal joint training: Synthetic data from image grounding datasets teaches the model to locate small targets before scaling to video
  3. Adaptive long-video encoding: Token compression preserves information uniformly across short and long videos, preventing the common problem where model performance degrades sharply after 5 minutes

Q3: Can Vidi2 replace human video editors?

Not entirely. Vidi2 excels at accelerating tedious tasks: finding clips, tracking objects, generating rough cuts. For creative decisions—pacing, emotional tone, artistic composition—human judgment remains essential. Current best practice is using Vidi2 as an intelligent assistant that handles 80% of the grunt work, letting editors focus on storytelling.

Q4: What are the computational requirements?

Based on official tests:

  • Inference: 12B model requires A100 40GB GPU for 30-min videos; 7B model runs on RTX 4090 24GB for 10-min videos
  • Latency: Expect 3-5 minutes processing time per 10 minutes of video
  • Training: Not publicly disclosed, but likely requires multi-GPU clusters with large-scale distributed training

Q5: How does Vidi2 handle occlusions and disappearing objects?

The VUE-STG benchmark intentionally includes temporally fragmented tubes where objects leave and re-enter the frame. Vidi2 is trained on such data, learning to stop tracking when objects disappear and resume when they return. The vIoU-Intersection metric (where Vidi2 scores 60.30%) specifically measures this capability.

Q6: What role does audio play in the model?

Vidi2 is truly multimodal. In VUE-TR-V2, queries that combine visual and audio cues achieve 46.81% tIoU with Vidi2, versus 37.26% for Gemini. The model processes audio spectrograms alongside visual frames, enabling queries like “find the scene where glass shatters and people react.”

Q7: Can I fine-tune Vidi2 for my specific domain (e.g., medical videos, sports)?

Yes. The recommended approach:

  1. Start with Vidi2-7B or 12B pretrained weights
  2. Freeze the vision encoder to preserve general visual understanding
  3. Add domain-specific data (your own annotated videos)
  4. Fine-tune with LoRA adapters on the language model component
  5. Evaluate using VUE-STG’s per-category metrics to identify improvement areas

Q8: Are the outputs in absolute or relative coordinates?

Both. The JSON output provides:

  • Absolute timestamps: Seconds from video start (e.g., 61.5)
  • Relative bounding boxes: Normalized coordinates [0,1] relative to frame width/height

This dual format makes it easy to:

  • Seek to exact moments in video players
  • Scale annotations across different resolutions
  • Compare results across varied video sources

Q9: Does the model hallucinate bounding boxes?

All models occasionally predict boxes where no object exists. Vidi2’s vP (spatio-temporal precision) of 44.56% indicates that about 55% of predicted frames contain actual objects. This is dramatically better than Gemini (8.95%) or GPT-5 (13.01%). In practice, setting a confidence threshold of 0.7 or higher eliminates most false positives.

Q10: What’s the commercial licensing status?

Vidi-7B weights are released under research license. Commercial use requires contacting ByteDance’s Intelligent Creation team. The technology already powers TikTok’s Smart Split and AI Outline features, indicating a path to production deployment for enterprise partners.

Industry Impact and Future Trajectory

Vidi2’s release signals a fundamental shift in video AI—from recognition to actionable understanding. Here are the key implications:

Benchmark Evolution Reflects Real Needs

The creation of VUE-STG and VUE-TR-V2 demonstrates that the research community recognizes a crucial gap: existing metrics don’t capture the messy reality of professional video editing. Future models will be judged on their ability to handle:

  • Multi-hour footage without performance degradation
  • Temporal discontinuities caused by cuts and occlusions
  • Small, fast-moving objects in cluttered scenes
  • Multimodal queries that combine visual, auditory, and semantic cues

Creative AI: From Tool to Collaborator

The progression from Vidi1 to Vidi2 mirrors a broader trend: AI systems are evolving from reactive tools that respond to commands to proactive partners that suggest creative directions. The “storyline-based video creation” capability hints at a future where creators describe a mood or theme, and AI handles shot selection, pacing, and even soundtrack curation.

Open-Source Competitive Advantage

Perhaps most significantly, Vidi2 proves that open-source models can surpass closed alternatives in specialized domains. While Gemini and GPT-5 lead in general multimodal benchmarks, Vidi2’s focused architecture and domain-specific training data give it a decisive edge in video grounding.

This suggests a future where:

  • Foundation models (like GPT, Gemini) provide broad capabilities
  • Specialized models (like Vidi2) excel at domain-specific tasks
  • Modular systems combine multiple models for最佳效果

Multimodal Fusion at Scale

Vidi2’s unified processing of text, vision, and audio points toward true multimodal architectures rather than stitched-together pipelines. The next frontier is handling higher temporal resolution (30+ FPS), 3D spatial understanding, and interactive video where user queries can be contextualized within the video timeline.

Limitations, Considerations, and Best Practices

No technology is perfect. Understanding Vidi2’s boundaries helps set realistic expectations.

Current Limitations

  1. Computational Cost: Processing a 30-minute video requires high-end GPU hardware and several minutes. Real-time applications remain challenging.
  2. Temporal Resolution: The 1FPS sampling strategy trades detail for efficiency. Fast actions lasting milliseconds might be missed.
  3. Data Distribution Bias: Performance is strongest on natural scenes similar to TikTok/YouTube content. Highly stylized content (animation, CGI, medical imaging) may require domain adaptation.
  4. Annotation Requirements: Creating training data like VUE-STG is labor-intensive, potentially limiting rapid domain expansion.

Responsible Use Guidelines

When deploying Vidi2, consider:

Privacy: The official examples blur faces. Implement automatic face detection and redaction before processing user-generated content.

Bias: Evaluate performance across different demographics, video quality levels, and cultural contexts. The benchmark includes diverse content, but your use case may have unique requirements.

Transparency: When used for content moderation or recommendation, disclose AI involvement to users.

Error Handling: Always include human review for critical applications. Vidi2’s vIoU of 32% means roughly 2/3 of predictions need refinement.

Recommended Implementation Pathway

For organizations looking to adopt Vidi2:

Phase 1: Pilot (2-4 weeks)

  • Run Vidi2-7B on a sample of your video data
  • Evaluate using VUE-STG metrics adapted to your domain
  • Identify failure modes and latency bottlenecks

Phase 2: Integration (4-8 weeks)

  • Deploy as a microservice with GPU orchestration
  • Build visualization UI for reviewer feedback
  • Implement confidence-based routing (high confidence → auto-approval, low → human review)

Phase 3: Customization (8+ weeks)

  • Collect domain-specific annotations
  • Fine-tune with LoRA or full fine-tuning
  • Iterate evaluation and deployment

Resource Summary

All resources mentioned in this guide are publicly available:

Citation

If you use Vidi2 in your research or product, please cite:

@article{Vidi2025vidi2,
  title={Vidi2: Large Multimodal Models for Video Understanding and Creation},
  author={Vidi Team, Celong Liu, Chia-Wen Kuo, Chuang Huang, Dawei Du, Fan Chen, Guang Chen, Haoji Zhang, Haojun Zhao, Lingxi Zhang, Lu Guo, Lusha Li, Longyin Wen, Qihang Fan, Qingyu Chen, Rachel Deng, Sijie Zhu, Stuart Siew, Tong Jin, Weiyan Tao, Wen Zhong, Xiaohui Shen, Xin Gu, Zhenfang Chen, Zuhua Lin},
  journal={arXiv preprint arXiv:2511.19529},
  year={2025}
}

For the original Vidi model:

@article{Vidi2025vidi,
  title={Vidi: Large Multimodal Models for Video Understanding and Editing},
  author={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},
  journal={arXiv preprint arXiv:2504.15681},
  year={2025}
}

Final Thoughts

Vidi2 represents a meaningful step toward intelligent video understanding that meets professional standards. Its ability to simultaneously reason about time and space, process long-form content, and maintain competitive QA performance makes it a versatile tool for researchers and practitioners alike.

The substantial performance gap over closed-source alternatives in spatio-temporal grounding suggests that domain-specialized architectures remain valuable even in the era of massive general-purpose models. For video creators, editors, and platform developers, Vidi2 offers a glimpse into a future where AI doesn’t just analyze content—it collaborates in its creation.

Start with the 7B model, run the benchmarks, and see how Vidi2 can transform your video workflows. The era of truly intelligent video editing has arrived, and it’s more accessible than you might think.