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Most of the previous image-based 3D human pose and mesh estimation methodsestimate parameters of the human mesh model from an input image. However,directly regressing the parameters from the input image is a highly non-linearmapping because it breaks the spatial relationship between pixels in the inputimage. In addition, it cannot model the prediction uncertainty, which can maketraining harder. To resolve the above issues, we propose I2L-MeshNet, animage-to-lixel (line+pixel) prediction network. The proposed I2L-MeshNetpredicts the per-lixel likelihood on 1D heatmaps for each mesh vertexcoordinate instead of directly regressing the parameters. Our lixel-based 1Dheatmap preserves the spatial relationship in the input image and models theprediction uncertainty. We demonstrate the benefit of the image-to-lixelprediction and show that the proposed I2L-MeshNet outperforms previous methods.The code is publicly available https://github.com/mks0601/I2L-MeshNet_RELEASE.

I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image