Teaching AI to make emotional videos is hard because there is no single correct answer. Fixed scoring rules for motion and visual clarity often miss emotional intent. Researchers created EmoReward, which learns from past training runs which cues, like facial expressions or colors, to trust more, and adjusts each cue's importance for every new video. Human judges preferred videos trained with EmoReward for emotional quality and visual coherence, showing that adapting reward weights over time improves emotional expression in generated videos.
AI video generation gains emotional fidelity when rewards trust only reliable cues
The authors combine per-video gating of face, motion and color cues with cross-run reliability memory to improve emotional video rewards.
Chinese Tech
Mingzhe Zheng · Yue Wu · Jing Tan · Chang Zou · Dengyang Jiang · Zeyue Tian · +16 more
The Hong Kong University of Science and Technology · Tencent Hunyuan · Fudan University · University of Waterloo
Research Digest··3 min read
In one line
EmoReward adaptively calibrates reward weights in emotional video generation using reliability learned from previous training run traces.
Before you read this
Three ideas this paper builds onagentic video generationMulti-agent systems collaborate autonomously to produce long-form videos maintaining narrative and visual coherence.18 papers →group relative policy optimizationA reinforcement learning method that optimizes policy by computing advantages based on group-level reward statistics.174 papers →reward hackingReward hacking occurs when an AI agent exploits loopholes to achieve high reward without meeting the intended objective.60 papers →
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The authors introduce EmoReward, a mechanism that calibrates which emotional cues a video reward model should trust, both within a generated video and across successive reinforcement learning runs.
The authors address emotional video generation, where reward models for visual quality, motion and text alignment do not capture intended emotion and can be exploited.
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