Compressing Human Body Video with Interactive Semantics: A Generative Approach
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916751236661248 |
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| author | Chen, Bolin Yin, Shanzhi Zhu, Hanwei Zhu, Lingyu Zhang, Zihan Chen, Jie Liao, Ru-Ling Wang, Shiqi Ye, Yan |
| author_facet | Chen, Bolin Yin, Shanzhi Zhu, Hanwei Zhu, Lingyu Zhang, Zihan Chen, Jie Liao, Ru-Ling Wang, Shiqi Ye, Yan |
| contents | In this paper, we propose to compress human body video with interactive semantics, which can facilitate video coding to be interactive and controllable by manipulating semantic-level representations embedded in the coded bitstream. In particular, the proposed encoder employs a 3D human model to disentangle nonlinear dynamics and complex motion of human body signal into a series of configurable embeddings, which are controllably edited, compactly compressed, and efficiently transmitted. Moreover, the proposed decoder can evolve the mesh-based motion fields from these decoded semantics to realize the high-quality human body video reconstruction. Experimental results illustrate that the proposed framework can achieve promising compression performance for human body videos at ultra-low bitrate ranges compared with the state-of-the-art video coding standard Versatile Video Coding (VVC) and the latest generative compression schemes. Furthermore, the proposed framework enables interactive human body video coding without any additional pre-/post-manipulation processes, which is expected to shed light on metaverse-related digital human communication in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16152 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Compressing Human Body Video with Interactive Semantics: A Generative Approach Chen, Bolin Yin, Shanzhi Zhu, Hanwei Zhu, Lingyu Zhang, Zihan Chen, Jie Liao, Ru-Ling Wang, Shiqi Ye, Yan Image and Video Processing Computer Vision and Pattern Recognition In this paper, we propose to compress human body video with interactive semantics, which can facilitate video coding to be interactive and controllable by manipulating semantic-level representations embedded in the coded bitstream. In particular, the proposed encoder employs a 3D human model to disentangle nonlinear dynamics and complex motion of human body signal into a series of configurable embeddings, which are controllably edited, compactly compressed, and efficiently transmitted. Moreover, the proposed decoder can evolve the mesh-based motion fields from these decoded semantics to realize the high-quality human body video reconstruction. Experimental results illustrate that the proposed framework can achieve promising compression performance for human body videos at ultra-low bitrate ranges compared with the state-of-the-art video coding standard Versatile Video Coding (VVC) and the latest generative compression schemes. Furthermore, the proposed framework enables interactive human body video coding without any additional pre-/post-manipulation processes, which is expected to shed light on metaverse-related digital human communication in the future. |
| title | Compressing Human Body Video with Interactive Semantics: A Generative Approach |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.16152 |