| --- |
| license: cc-by-nc-4.0 |
| pretty_name: SVI-Bench |
| gated: true |
| extra_gated_prompt: > |
| Access to SVI-Bench is provided only for approved research and educational |
| use. Access requests must be submitted using an institutional .edu email |
| address. Please describe your intended use. By requesting access, you agree to |
| the SVI-Bench Dataset Access and Non-Disclosure Terms in the dataset card. |
| extra_gated_fields: |
| Full legal name: text |
| Institutional email: text |
| Affiliation: text |
| Role: text |
| Principal investigator: text |
| Country: country |
| Intended use: |
| type: select |
| options: |
| - Non commercial research |
| - Education |
| - Reproducibility and benchmark evaluation |
| - label: Commercial evaluation requires separate written approval |
| value: commercial_review |
| - label: Other please describe |
| value: other |
| Intended use description: text |
| Share access with collaborators: |
| type: select |
| options: |
| - 'No' |
| - Yes only collaborators at my institution |
| - Yes external collaborators with written approval |
| No redistribution: checkbox |
| No credential sharing: checkbox |
| Non commercial use only: checkbox |
| No commercial products without approval: checkbox |
| No source video reconstruction or mirroring: checkbox |
| No surveillance biometric identification betting or harmful profiling: checkbox |
| Report security incidents: checkbox |
| Agree to dataset terms: checkbox |
| I confirm my institutional email address is a valid edu address: checkbox |
| task_categories: |
| - video-text-to-text |
| - visual-question-answering |
| language: |
| - en |
| tags: |
| - sports-video-understanding |
| - video-understanding |
| - video-question-answering |
| - video-captioning |
| - video-retrieval |
| - video-generation |
| - multimodal |
| - benchmark |
| - strategic-reasoning |
| - simulation |
| - agentic-reasoning |
| - non-commercial |
| --- |
| |
|
|
| # SVI-Bench |
|
|
| **SVI-Bench** is a benchmark for **Strategic Video Intelligence**: evaluating whether multimodal models can move from seeing what happens in complex multi-agent sports video to explaining why it happens, simulating alternatives, and autonomously integrating evidence for higher-level analysis. |
|
|
| SVI-Bench spans **basketball, hockey, and soccer**, combining broadcast video with aligned play-by-play logs, expert commentary, game reports, and structured statistics. The benchmark organizes **nine tasks** across four capability pillars: |
|
|
| * **Perception:** parse short video clips into structured descriptions, answer fine-grained action questions, and retrieve clips from compositional natural-language queries. |
| * **Reasoning:** explain strategic events, forecast future outcomes from partial game context, and synthesize long-form narratives from full-game evidence. |
| * **Simulation:** generate plausible future play sequences from motion context and produce goal-conditioned actions that satisfy strategic objectives. |
| * **Agency:** autonomously gather and integrate evidence across large-scale multimodal sports corpora—including video, event logs, commentary, reports, and statistics—to answer complex analytical questions. |
|
|
| SVI-Bench is designed for research on video-language models, sports analytics, long-horizon video understanding, video retrieval, controllable video generation, and agentic reasoning over multimodal sports corpora. |
|
|
| ## Links |
|
|
| | Resource | Link | |
| | --------------------------- | ---------------------------------------------------------------------------------------------------------- | |
| | Project page | [https://svi-bench.github.io/](https://svi-bench.github.io/) | |
| | Code & Github Page | [https://github.com/Texaser/SVI-Bench](https://github.com/Texaser/SVI-Bench) | |
| | Arxiv | [http://arxiv.org/abs/2605.31529](http://arxiv.org/abs/2605.31529) | |
| | Extended paper | [https://svi-bench.github.io/svi_bench_extended.pdf](https://svi-bench.github.io/svi_bench_extended.pdf) | |
|
|
|
|
| ## Dataset Summary |
|
|
| SVI-Bench provides task-specific data, videos, annotations, embeddings, checkpoints, tracker weights, questions, and evaluation assets for a multi-task sports video benchmark. Unlike single-task video QA or captioning datasets, SVI-Bench is structured to test a progression of model abilities: from localized perception and action recognition, to strategic understanding, to future prediction and action generation, to agentic reasoning across a large sports-video corpus. |
|
|
| The benchmark is organized into nine task directories (`T1`–`T9`). Each task includes sport-specific data or evaluation assets where applicable. Some tasks focus on classic video-language understanding, while others require retrieval, forecasting, generation, or tool/agent-style reasoning. |
|
|
| ## Supported Tasks |
|
|
| | ID | Task | Sports | Pillar | |
| | -- | ----------------------------------- | -------------------------- | ---------------------- | |
| | T1 | Structured play-by-play description | Basketball, hockey, soccer | Perception | |
| | T2 | Fine-grained action QA | Basketball, hockey, soccer | Perception | |
| | T3 | Compositional video retrieval | Basketball, hockey, soccer | Perception | |
| | T4 | Strategic reasoning QA | Basketball, hockey, soccer | Reasoning | |
| | T5 | Outcome forecasting | Basketball, soccer | Reasoning | |
| | T6 | Long-form narrative synthesis | Basketball, hockey, soccer | Reasoning | |
| | T7 | Motion-conditioned generation | Basketball, soccer | Simulation | |
| | T8 | Goal-conditioned action generation | Basketball | Simulation | |
| | T9 | Cross-corpus agentic reasoning | Basketball, hockey, soccer | Agency | |
|
|
| ## Repository Layout |
|
|
| ```text |
| T1/{basketball,hockey,soccer,captions}/ Structured play-by-play description |
| T2/{basketball,hockey,soccer,data}/ Fine-grained action QA |
| T3/{clips,ckpts,embeds,compositions,data}/ Compositional video retrieval |
| T4/{basketball,hockey,soccer}/ Strategic reasoning QA |
| T5/{basketball,soccer}/ Outcome forecasting |
| T6/soccer/ Long-form narrative synthesis |
| T7/{basketball,soccer}/ Motion-conditioned generation |
| T7/tracker_weights/ YOLOX + MixFormer-ViT tracker weights shared with T8 |
| T8/basketball/ Goal-conditioned action generation |
| T8/llava_qa_checkpoint/ Fine-tuned LLaVA-Qwen QA model |
| T8/tracker_weights/ YOLOX + MixFormer-ViT tracker weights |
| T9/{data,ckpts,embeds,questions,storage}/ Cross-corpus agentic reasoning |
| ``` |
|
|
| ## Intended Uses |
|
|
| SVI-Bench is intended for: |
|
|
| * evaluating multimodal and video-language models on sports video understanding; |
| * benchmarking long-horizon temporal reasoning, action recognition, retrieval, and forecasting; |
| * studying strategic understanding in dynamic multi-agent sports environments; |
| * developing reproducible evaluation pipelines for video QA, captioning, retrieval, generation, and agentic reasoning; |
| * non-commercial research and educational use. |
|
|
| ## Out-of-Scope Uses |
|
|
| SVI-Bench is **not** intended for: |
|
|
| * commercial products, services, or model training without separate written approval; |
| * redistribution, mirroring, or re-hosting of source videos or restricted assets; |
| * surveillance, biometric identification, athlete profiling, betting, gambling, or other harmful profiling applications; |
| * attempts to reconstruct restricted source content outside the approved dataset access flow; |
| * use that violates the dataset license or access terms. |
|
|
| ## Access |
|
|
| This is a gated dataset. To request access, use the Hugging Face access form using **your institutional .edu email account** and provide your affiliation, intended use, and agreement to the dataset terms. Access is limited to approved research and educational use. |
|
|
| Because the repository is large and includes video assets, embeddings, checkpoints, tracker weights, and task-specific data, users should download only the task directories needed for their experiments. |
|
|
| ## Quick Start |
|
|
| ### 1. Request dataset access |
|
|
| Complete the access request form on Hugging Face. We will manually review your information and approve access to the dataset. |
|
|
| ### 2. Install dependencies |
|
|
| Access the code and scripts on GitHub. Use the task-specific README files and the code repository for the latest dependencies and evaluation instructions. |
|
|
| ```bash |
| git clone https://github.com/Texaser/SVI-Bench.git |
| cd SVI-Bench |
| ``` |
|
|
| Install the dependencies specified in the repository or the per-task README files. |
|
|
| ### 3. Download the data |
|
|
| After your Hugging Face access request is approved, you can download only the needed task directory. For example: |
|
|
| ```bash |
| # Example: download T2 data repository after approval |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download( |
| repo_id="MVP-Group/SVI-Bench", |
| repo_type="dataset", |
| allow_patterns=[ |
| "T2/basketball/*", |
| ], |
| local_dir="SVI-Bench/T2/basketball", |
| ) |
| ``` |
|
|
| For large files, consider using selective download patterns, `huggingface_hub`, or `git lfs` include/exclude rules so you do not download the full dataset unnecessarily. |
|
|
| ### 4. Run evaluation |
|
|
| Evaluation scripts and task-specific instructions are maintained in the code repository: |
|
|
| ```text |
| https://github.com/Texaser/SVI-Bench |
| ``` |
|
|
| Each task may have its own expected input format, model output format, metrics, and preprocessing requirements. Refer to the corresponding task README before running experiments. |
|
|
|
|
| ## Licensing and Terms |
|
|
| SVI-Bench is released under **CC BY-NC 4.0** for approved non-commercial research and educational use only. Redistribution is not permitted. Users must also follow the gated access terms shown on the Hugging Face request form. |
|
|
| ## Ethical Considerations |
|
|
| Sports video datasets can involve athletes, spectators, and broadcast or third-party footage. Users should respect privacy, licensing, and data-use restrictions. Do not use this dataset for surveillance, biometric identification, harmful profiling, gambling/betting systems, or unauthorized commercial deployments. Report security or access incidents to the maintainers. |
|
|
| ## Citation |
|
|
| If you use SVI-Bench, please cite the benchmark: |
|
|
| ``` |
| @misc{pan2026svibenchdynamicmicroworldstrategic, |
| title={SVI-Bench: A Dynamic Microworld for Strategic Video Intelligence}, |
| author={Yulu Pan and Han Yi and Seongsu Ha and Md Mohaiminul Islam and Benjamin Zhang and Lorenzo Torresani and Gedas Bertasius}, |
| year={2026}, |
| eprint={2605.31529}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2605.31529}, |
| } |
| ``` |
|
|
| ## Contact |
|
|
| For questions about access, licensing, evaluation, or benchmark maintenance, please open an issue in the code repository or contact the SVI-Bench maintainers through the project page. |
