VideoMolmo: Spatio-Temporal Grounding Meets Pointing

TL;DR

VideoMolmo is a novel multimodal model that enhances spatio-temporal localization in videos by integrating language understanding, temporal consistency, and a new dataset, significantly improving pointing accuracy and reasoning across diverse real-world scenarios.

Contribution

We introduce VideoMolmo, a large multimodal model with a temporal module and mask fusion pipeline, along with a new dataset and benchmark for spatio-temporal pointing conditioned on text.

Findings

Outperforms existing models in spatio-temporal pointing accuracy.

Demonstrates strong generalization on out-of-distribution benchmarks.

Provides a new dataset and benchmark for future research.

Abstract

Spatio-temporal localization is vital for precise interactions across diverse domains, from biological research to autonomous navigation and interactive interfaces. Current video-based approaches, while proficient in tracking, lack the sophisticated reasoning capabilities of large language models, limiting their contextual understanding and generalization. We introduce VideoMolmo, a large multimodal model tailored for fine-grained spatio-temporal pointing conditioned on textual descriptions. Building upon the Molmo architecture, VideoMolmo incorporates a temporal module utilizing an attention mechanism to condition each frame on preceding frames, ensuring temporal consistency. Additionally, our novel temporal mask fusion pipeline employs SAM2 for bidirectional point propagation, significantly enhancing coherence across video sequences. This two-step decomposition, i.e., first using the LLM to generate precise pointing coordinates, then relying on a sequential mask-fusion module to produce coherent segmentation, not only simplifies the task for the language model but also enhances interpretability. Due to the lack of suitable datasets, we curate a comprehensive dataset comprising 72k video-caption pairs annotated with 100k object points. To evaluate the generalization of VideoMolmo, we introduce VPoS-Bench, a challenging out-of-distribution benchmark spanning five real-world scenarios: Cell Tracking, Egocentric Vision, Autonomous Driving, Video-GUI Interaction, and Robotics. We also evaluate our model on Referring Video Object Segmentation (Refer-VOS) and Reasoning VOS tasks. In comparison to existing models, VideoMolmo substantially improves spatio-temporal pointing accuracy and reasoning capability. Our code and models are publicly available at https://github.com/mbzuai-oryx/VideoMolmo.