Mem-MLP: Real-Time 3D Human Motion Generation from Sparse Inputs
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917095872135168 |
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| author | Mutlu, Sinan Angelis, Georgios F. Ozkan, Savas Wisbey, Paul Drosou, Anastasios Ozay, Mete |
| author_facet | Mutlu, Sinan Angelis, Georgios F. Ozkan, Savas Wisbey, Paul Drosou, Anastasios Ozay, Mete |
| contents | Realistic and smooth full-body tracking is crucial for immersive AR/VR applications. Existing systems primarily track head and hands via Head Mounted Devices (HMDs) and controllers, making the 3D full-body reconstruction in-complete. One potential approach is to generate the full-body motions from sparse inputs collected from limited sensors using a Neural Network (NN) model. In this paper, we propose a novel method based on a multi-layer perceptron (MLP) backbone that is enhanced with residual connections and a novel NN-component called Memory-Block. In particular, Memory-Block represents missing sensor data with trainable code-vectors, which are combined with the sparse signals from previous time instances to improve the temporal consistency. Furthermore, we formulate our solution as a multi-task learning problem, allowing our MLP-backbone to learn robust representations that boost accuracy. Our experiments show that our method outperforms state-of-the-art baselines by substantially reducing prediction errors. Moreover, it achieves 72 FPS on mobile HMDs that ultimately improves the accuracy-running time tradeoff. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16264 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mem-MLP: Real-Time 3D Human Motion Generation from Sparse Inputs Mutlu, Sinan Angelis, Georgios F. Ozkan, Savas Wisbey, Paul Drosou, Anastasios Ozay, Mete Computer Vision and Pattern Recognition Realistic and smooth full-body tracking is crucial for immersive AR/VR applications. Existing systems primarily track head and hands via Head Mounted Devices (HMDs) and controllers, making the 3D full-body reconstruction in-complete. One potential approach is to generate the full-body motions from sparse inputs collected from limited sensors using a Neural Network (NN) model. In this paper, we propose a novel method based on a multi-layer perceptron (MLP) backbone that is enhanced with residual connections and a novel NN-component called Memory-Block. In particular, Memory-Block represents missing sensor data with trainable code-vectors, which are combined with the sparse signals from previous time instances to improve the temporal consistency. Furthermore, we formulate our solution as a multi-task learning problem, allowing our MLP-backbone to learn robust representations that boost accuracy. Our experiments show that our method outperforms state-of-the-art baselines by substantially reducing prediction errors. Moreover, it achieves 72 FPS on mobile HMDs that ultimately improves the accuracy-running time tradeoff. |
| title | Mem-MLP: Real-Time 3D Human Motion Generation from Sparse Inputs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.16264 |