Dynamic Appearance Modelling from Minimal Cameras
Dynamic Appearance Modelling from Minimal Cameras
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DOI：
10.1109/cvprw53098.2021.00195
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Lewis Bridgeman;Jean-Yves Guillemaut;A. Hilton
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文献类型：
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作者：
Lewis Bridgeman;Jean-Yves Guillemaut;A. Hilton
我们提出了一种从最少的相机集对动态纹理外观进行建模的新颖方法。以前从多视图视频中捕捉人体动态外观的方法依赖于大型、昂贵的相机设置，并且通常逐帧存储纹理。我们将参数化的人体模型拟合到来自最少摄像机（少至 3 个）的多视图视频，并将来自多个视点和帧的部分纹理观察结合到学习框架中，以在给定输入姿势的情况下生成具有动态细节的全身纹理。我们方法的关键是我们的多频带损失函数，它将单独的混合函数应用于高和低空间频率以减少纹理伪影。我们在一系列多视图数据集上评估我们的方法，并表明我们的模型能够准确地生成全身动态纹理，即使只有部分相机覆盖。我们证明我们的方法在最少的相机设置上优于其他纹理生成方法。
We present a novel method for modelling dynamic texture appearance from a minimal set of cameras. Previous methods to capture the dynamic appearance of a human from multi-view video have relied on large, expensive camera setups, and typically store texture on a frame-by-frame basis. We fit a parameterised human body model to multi-view video from minimal cameras (as few as 3), and combine the partial texture observations from multiple viewpoints and frames in a learned framework to generate full-body textures with dynamic details given an input pose. Key to our method are our multi-band loss functions, which apply separate blending functions to the high and low spatial frequencies to reduce texture artefacts. We evaluate our method on a range of multi-view datasets, and show that our model is able to accurately produce full-body dynamic textures, even with only partial camera coverage. We demonstrate that our method outperforms other texture generation methods on minimal camera setups.