UniVBench: Towards Unified Evaluation for Video Foundation Models

TL;DR

UniVBench is introduced, 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.

Abstract

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.

Figures (26)

• Figure 1: Overview of the UniVBench evaluation setting across 8 Dimensions, 21 Sub-Dimensions, and 6 Tasks. Given a source video, T2V synthesizes a video using its ground-truth caption, while V2V reconstructs the video based solely on the model’s self-generated understanding text, enabling a direct diagnosis of perception–generation coupling. UniVBench supports six unified tasks—video captioning (V2T), text-to-video generation (T2V), reference-image video generation (R2V), text-instruction video editing (TV2V), reference-image video editing (RV2V), and video reconstruction (V2V).
• Figure 2: Workflow of UniV-Eval. The system accepts arbitrary inputs within a task setting and performs dynamic evaluation after planning and decomposition. The final results are delivered as a fine-grained checklist, providing traceable feedback for training optimization.
• Figure 3: Case Study Analysis of UniVBench in T2V and Reconstruction Task. T2V generation uses the ground truth text of the video, while V2V reconstruction relies on model's understanidng text. The generated videos are selected from OmniVideo
• Figure 4: An example of evaluation using different metrics, where the blue-highlighted part shows that UniV-Eval provides more detailed, traceable validation and assessment.
• Figure 5: Human expert annotations used to validate the reliability of UniV-Eval.