# EmoTrace-Bench: Diagnosing Emotional Expression in Text-to-Video Generation through Controlled Cues and Intensity

arXiv: https://arxiv.org/abs/2610.15452 · HTML: https://mlsanity.com/paper/2610.15452/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 11 Oct 2026.

## Builds on (most cited first, 13 of 13)

- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [EvalCrafter: Benchmarking and Evaluating Large Video Generation Models](https://mlsanity.com/paper/2310.11440/) (2023)
- [VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness](https://mlsanity.com/paper/2503.21755/) (2025)
- [T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation](https://mlsanity.com/paper/2407.14505/) (2024)
- [Video-Bench: Human-Aligned Video Generation Benchmark](https://mlsanity.com/paper/2504.04907/) (2025)
- [VABench: A Comprehensive Benchmark for Audio-Video Generation](https://mlsanity.com/paper/2512.09299/) (2025)
- [T2AV-Compass: Towards Unified Evaluation for Text-to-Audio-Video Generation](https://mlsanity.com/paper/2512.21094/) (2025)
- [EmoTalker: Emotionally Editable Talking Face Generation via Diffusion Model](https://mlsanity.com/paper/2401.08049/) (2024)
- [Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional Video Generation](https://mlsanity.com/paper/2509.26555/) (2025)
- [FilmBench: A Film-Grade Benchmark for Cinematic Video Generation](https://mlsanity.com/paper/2607.24241/) (2026)
- [EmoVid: A Multimodal Emotion Video Dataset for Emotion-Centric Video Understanding and Generation](https://mlsanity.com/paper/2511.11002/) (2025)
- [EmoWorld: A Decoupled Affective Field for Controllable Emotional Video Generation](https://mlsanity.com/paper/2608.06231/) (2026)
- [HUG-VIS: A Multimodal Benchmark for Human-centered Understanding and Generation in Visual Intelligence](https://mlsanity.com/paper/2608.26517/) (2026)

## Abstract (arXiv)

Text-to-video (T2V) models are increasingly used to generate videos with targeted emotional expressions, yet existing evaluation methods mainly rely on holistic video-text alignment and provide limited insight into whether failures arise from target-emotion expression, intensity control, or specific affective cues. We introduce EmoTrace-Bench, a fine-grained diagnostic benchmark that controls emotion intensity and selected affective cues in prompts and evaluates generated videos at multiple levels. EmoTrace-Bench contains 448 controlled prompts covering eight emotions, two intensity levels, and diverse cue conditions. Using 12 T2V models, we generate 5,376 videos and evaluate target-emotion presence, perceived intensity, and ten audiovisual dimensions of emotional expression with an automated evaluator calibrated and validated against human annotations on a subset of the data. Results reveal substantial differences across models and prompt designs. Prompts that explicitly describe a character's emotional reaction are followed more reliably than prompts that describe only events or scenes, whereas adding more affective cues or using more explicit cinematic audiovisual language does not consistently improve emotional generation. These results illustrate how aggregate alignment scores may overlook localized differences in target-emotion expression, intensity control, and cue realization. EmoTrace-Bench provides a traceable and interpretable framework for diagnosing emotional video generation without modifying existing generation pipelines, and we release the prompts and the automated evaluation rules.