Towards Film-Making Production Dialogue, Narration, Monologue Adaptive Moving Dubbing Benchmarks
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908346936721408 |
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| author | Wang, Chaoyi Zheng, Junjie Chen, Zihao Xia, Shiyu Ding, Chaofan Zhang, Xiaohao Tao, Xi He, Xiaoming Di, Xinhan |
| author_facet | Wang, Chaoyi Zheng, Junjie Chen, Zihao Xia, Shiyu Ding, Chaofan Zhang, Xiaohao Tao, Xi He, Xiaoming Di, Xinhan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01450 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Film-Making Production Dialogue, Narration, Monologue Adaptive Moving Dubbing Benchmarks Wang, Chaoyi Zheng, Junjie Chen, Zihao Xia, Shiyu Ding, Chaofan Zhang, Xiaohao Tao, Xi He, Xiaoming Di, Xinhan Machine Learning 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. |
| title | Towards Film-Making Production Dialogue, Narration, Monologue Adaptive Moving Dubbing Benchmarks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.01450 |