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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.23169 |
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| _version_ | 1866914060873760768 |
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| author | Chen, Bolin Liao, Ru-Ling Ye, Yan Chen, Jie Yin, Shanzhi Ju, Xinrui Wang, Shiqi Fan, Yibo |
| author_facet | Chen, Bolin Liao, Ru-Ling Ye, Yan Chen, Jie Yin, Shanzhi Ju, Xinrui Wang, Shiqi Fan, Yibo |
| contents | For bandwidth-constrained multimedia applications, simultaneously achieving ultra-low bitrate human video compression and accurate vertex prediction remains a critical challenge, as it demands the harmonization of dynamic motion modeling, detailed appearance synthesis, and geometric consistency. To address this challenge, we propose Sparse2Dense, a keypoint-driven generative framework that leverages extremely sparse 3D keypoints as compact transmitted symbols to enable ultra-low bitrate human video compression and precise human vertex prediction. The key innovation is the multi-task learning-based and keypoint-aware deep generative model, which could encode complex human motion via compact 3D keypoints and leverage these sparse keypoints to estimate dense motion for video synthesis with temporal coherence and realistic textures. Additionally, a vertex predictor is integrated to learn human vertex geometry through joint optimization with video generation, ensuring alignment between visual content and geometric structure. Extensive experiments demonstrate that the proposed Sparse2Dense framework achieves competitive compression performance for human video over traditional/generative video codecs, whilst enabling precise human vertex prediction for downstream geometry applications. As such, Sparse2Dense is expected to facilitate bandwidth-efficient human-centric media transmission, such as real-time motion analysis, virtual human animation, and immersive entertainment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23169 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sparse2Dense: A Keypoint-driven Generative Framework for Human Video Compression and Vertex Prediction Chen, Bolin Liao, Ru-Ling Ye, Yan Chen, Jie Yin, Shanzhi Ju, Xinrui Wang, Shiqi Fan, Yibo Computer Vision and Pattern Recognition For bandwidth-constrained multimedia applications, simultaneously achieving ultra-low bitrate human video compression and accurate vertex prediction remains a critical challenge, as it demands the harmonization of dynamic motion modeling, detailed appearance synthesis, and geometric consistency. To address this challenge, we propose Sparse2Dense, a keypoint-driven generative framework that leverages extremely sparse 3D keypoints as compact transmitted symbols to enable ultra-low bitrate human video compression and precise human vertex prediction. The key innovation is the multi-task learning-based and keypoint-aware deep generative model, which could encode complex human motion via compact 3D keypoints and leverage these sparse keypoints to estimate dense motion for video synthesis with temporal coherence and realistic textures. Additionally, a vertex predictor is integrated to learn human vertex geometry through joint optimization with video generation, ensuring alignment between visual content and geometric structure. Extensive experiments demonstrate that the proposed Sparse2Dense framework achieves competitive compression performance for human video over traditional/generative video codecs, whilst enabling precise human vertex prediction for downstream geometry applications. As such, Sparse2Dense is expected to facilitate bandwidth-efficient human-centric media transmission, such as real-time motion analysis, virtual human animation, and immersive entertainment. |
| title | Sparse2Dense: A Keypoint-driven Generative Framework for Human Video Compression and Vertex Prediction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.23169 |