Benchmarking Dynamic Affective Reasoning: A Viewer-Centric Video Emotion Dataset

Video emotion analysis is typically framed as a static clas-sification problem, treating each clip as an independent labeled unit.However, such a formulation overlooks a key psychological fact: emotionschange as a result of cumulative reactions to consecutive causal events. Tobridge this gap, we introduce DAR (Dynamic Affective Reasoning), thefirst large-scale benchmark for viewer-centric affect transitions and causalreasoning over consecutive video events. DAR contains 15,087 videos and36,908 event-aligned affective segments annotated with 27 emotion cat-egories. Unlike existing video-based emotion datasets, DAR presents anew viewer-centric perspective on fine-grained emotional expressions andtransitions, and provides dense, temporally grounded, and causally ex-plicit reasoning chains. Based on DAR, we formally define three challeng-ing tasks: affective segmentation, fine-grained emotion classification, andaffective reasoning. Complementing this benchmark, we propose DAR-R1, a two-stage framework that combines supervised fine-tuning withGroup Relative Policy Optimization. Experiments across 10+ MLLMsshow that DAR-R1 sets a new state-of-the-art for dynamic affective rea-soning, in terms of both emotional localization and affective reasoning.Project page: https://github.com/Zhang-Zhiyan/DAR.