Existing video-language models can generate factual descriptions of road events but lack control over how these events are expressed: their tone, urgency, or style. This limits deployment in communication-critical settings where the effectiveness of a message depends on both content and presentation, not just factual accuracy. To mitigate this, we introduce a comprehensive dataset-model-evaluation suite for tone-controllable road video captioning. Our human-validated data generation pipeline expands road-video corpora with diverse tonal annotations and multi-tone captions, yielding the RoadTones-51K dataset. We propose RoadTones-VL-CoT, a controllable video-to-text model that also generates tone-conditioned Chain-of-Thought intermediate drafts for interpretability. We also introduce RoadTones-Eval, a new evaluation suite that jointly measures factual consistency and tone adherence. In addition, we conducted a user study whose results validate caption quality, tone control, and factual consistency. Together, these contributions lay the foundation for context-sensitive tone-controllable video captioning.
@article{arxiv.2605.21411,
title = {RoadTones: Tone Controllable Text Generation from Road Event Videos},
author = {Chirag Parikh and Siddhi Pravin Lipare and Ravi Kiran Sarvadevabhatla},
journal= {arXiv preprint arXiv:2605.21411},
year = {2026}
}
Comments
Accepted at CVPR Findings 2026. Project page: https://roadtones.github.io/