Video Action Differencing
Authors: name: James Burgess, name: Xiaohan Wang, name: Yuhui Zhang, name: Anita Rau, name: Alejandro Lozano, name: Lisa Dunlap, name: Trevor Darrell, name: Serena Yeung-Levy

How do two individuals differ when performing the same action? In this work,
we introduce Video Action Differencing (VidDiff), the novel task of identifying
subtle differences between videos of the same action, which has many
applications, such as coaching and skill learning. To enable development on
this new task, we first create VidDiffBench, a benchmark dataset containing 549
video pairs, with human annotations of 4,469 fine-grained action differences
and 2,075 localization timestamps indicating where these differences occur. Our
experiments demonstrate that VidDiffBench poses a significant challenge for
state-of-the-art large multimodal models (LMMs), such as GPT-4o and Qwen2-VL.
By analyzing failure cases of LMMs on VidDiffBench, we highlight two key
challenges for this task: localizing relevant sub-actions over two videos and
fine-grained frame comparison. To overcome these, we propose the VidDiff
method, an agentic workflow that breaks the task into three stages: action
difference proposal, keyframe localization, and frame differencing, each stage
utilizing specialized foundation models. To encourage future research in this
new task, we release the benchmark at
https://huggingface.co/datasets/jmhb/VidDiffBench and code at
http://jmhb0.github.io/viddiff.