# UniVBench: Towards Unified Evaluation for Video Foundation Models

arXiv: https://arxiv.org/abs/2602.21835 · HTML: https://mlsanity.com/paper/2602.21835/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 4 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Built on by (most cited first, 13 of 13)

- [MSAVBench: Towards Comprehensive and Reliable Evaluation of Multi-Shot Audio-Video Generation](https://mlsanity.com/paper/2605.20183/) (2026)
- [VideoWeaver: Evaluating and Evolving Skills for Agentic Long Video Generation](https://mlsanity.com/paper/2606.08091/) (2026)
- [EvalVerse: Pipeline-Aware and Expert-Calibrated Benchmarking for Professional Cinematic Video Generation](https://mlsanity.com/paper/2605.23271/) (2026)
- [VideoArgus: Agentic Rubric-Grounded Unified Evaluation for Video Generation and Editing](https://mlsanity.com/paper/2608.05485/) (2026)
- [CoVEBench: Can Video Editing Models Handle Complex Instructions?](https://mlsanity.com/paper/2606.08415/) (2026)
- [ReBind: Multi-Reference Video Editing via Structured Instructions with Explicit Reference Relationships](https://mlsanity.com/paper/2607.14681/) (2026)
- [Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos](https://mlsanity.com/paper/2603.17693/) (2026)
- [How Far Are Video Models from True Multimodal Reasoning?](https://mlsanity.com/paper/2604.19193/) (2026)
- [AVE-Compass: Towards Holistic Evaluation for Audio-Video Editing Abilities](https://mlsanity.com/paper/2607.24821/) (2026)
- [PersonaShot: Benchmarking Person-Centric Narrative Continuity in Multi-Shot Video Generation](https://mlsanity.com/paper/2608.16717/) (2026)
- [OmniVBench: A Benchmark and Large-Scale Dataset for Omni Reference-to-Video Generation](https://mlsanity.com/paper/2609.22069/) (2026)
- [ORAV: Benchmarking Audio-Video Generation from Multimodal Contexts](https://mlsanity.com/paper/2609.34843/) (2026)
- [Towards Unified Evaluation of Prompt Enhancers for Video Generation](https://mlsanity.com/paper/2610.11736/) (2026)

## Abstract (arXiv)

Video foundation models aim to integrate video understanding, generation, editing, and instruction following within a single framework, making them a central direction for next-generation multimodal systems. However, existing evaluation benchmarks remain fragmented and limited in scope, as they each target a single task, rely on task-specific metrics, and typically use short or simple video clips. As a result, they do not capture the unified capabilities that these models are designed to deliver. To address this gap, we introduce UniVBench, a benchmark purpose-built for evaluating video foundation models across four core abilities: video understanding, video generation, video editing, and a newly proposed task, video reconstruction, which assesses how faithfully a model can reproduce video content it has encountered. Our benchmark substantially expands the complexity of evaluation by incorporating 200 high-quality, diverse and multi-shot videos, each paired with detailed captions, multi-format editing instructions, and reference images. All videos are human-created and carefully validated, offering richer cinematic information than prior benchmarks. In addition, we develop a unified agentic evaluation system (UniV-Eval) that standardizes prompting, instruction parsing, and scoring across all tasks, enabling fair, scalable, and reproducible comparisons of unified video models. By grounding evaluation in instruction-based multi-shot video tasks, UniVBench provides the first framework for measuring the integrated capabilities that video foundation models aim to achieve. Extensive human annotations ensure our evaluation aligns with human judgment, enabling rigorous assessment and accelerating progress toward robust video intelligence.