# Towards Film-Making Production Dialogue, Narration, Monologue Adaptive Moving Dubbing Benchmarks

arXiv: https://arxiv.org/abs/2505.01450 · HTML: https://mlsanity.com/paper/2505.01450/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 27 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 21 of 21)

- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [LLaVA-CoT: Let Vision Language Models Reason Step-by-Step](https://mlsanity.com/paper/2411.10440/) (2024)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment](https://mlsanity.com/paper/2203.07190/) (2022)
- [Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesis](https://mlsanity.com/paper/1910.10288/) (2019)
- [Neural Dubber: Dubbing for Videos According to Scripts](https://mlsanity.com/paper/2110.08243/) (2021)
- [Learning to Dub Movies via Hierarchical Prosody Models](https://mlsanity.com/paper/2212.04054/) (2022)
- [Thinking Before Looking: Improving Multimodal LLM Reasoning via Mitigating Visual Hallucination](https://mlsanity.com/paper/2411.12591/) (2024)
- [Imaginary Voice: Face-styled Diffusion Model for Text-to-Speech](https://mlsanity.com/paper/2302.13700/) (2023)
- [From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities](https://mlsanity.com/paper/2311.00308/) (2023)
- [V2C: Visual Voice Cloning](https://mlsanity.com/paper/2111.12890/) (2021)
- [LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading](https://mlsanity.com/paper/2306.03258/) (2023)
- [StyleDubber: Towards Multi-Scale Style Learning for Movie Dubbing](https://mlsanity.com/paper/2402.12636/) (2024)
- [VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding](https://mlsanity.com/paper/2412.02186/) (2024)
- [EmoDubber: Towards High Quality and Emotion Controllable Movie Dubbing](https://mlsanity.com/paper/2412.08988/) (2024)
- [More than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech](https://mlsanity.com/paper/2111.10139/) (2021)
- [Visual RAG: Expanding MLLM visual knowledge without fine-tuning](https://mlsanity.com/paper/2501.10834/) (2025)
- [DubWise: Video-Guided Speech Duration Control in Multimodal LLM-based Text-to-Speech for Dubbing](https://mlsanity.com/paper/2406.08802/) (2024)
- [MCDubber: Multimodal Context-Aware Expressive Video Dubbing](https://mlsanity.com/paper/2408.11593/) (2024)
- [Autonomous Imagination: Closed-Loop Decomposition of Visual-to-Textual Conversion in Visual Reasoning for Multimodal Large Language Models](https://mlsanity.com/paper/2411.18142/) (2024)

## Abstract (arXiv)

Movie dubbing has advanced significantly, yet assessing the real-world effectiveness of these models remains challenging. A comprehensive evaluation benchmark is crucial for two key reasons: 1) Existing metrics fail to fully capture the complexities of dialogue, narration, monologue, and actor adaptability in movie dubbing. 2) A practical evaluation system should offer valuable insights to improve movie dubbing quality and advancement in film production. To this end, we introduce Talking Adaptive Dubbing Benchmarks (TA-Dubbing), designed to improve film production by adapting to dialogue, narration, monologue, and actors in movie dubbing. TA-Dubbing offers several key advantages: 1) Comprehensive Dimensions: TA-Dubbing covers a variety of dimensions of movie dubbing, incorporating metric evaluations for both movie understanding and speech generation. 2) Versatile Benchmarking: TA-Dubbing is designed to evaluate state-of-the-art movie dubbing models and advanced multi-modal large language models. 3) Full Open-Sourcing: We fully open-source TA-Dubbing at https://github.com/woka- 0a/DeepDubber- V1 including all video suits, evaluation methods, annotations. We also continuously integrate new movie dubbing models into the TA-Dubbing leaderboard at https://github.com/woka- 0a/DeepDubber-V1 to drive forward the field of movie dubbing.