![]() ![]() We demonstrate that our approach significantly outperforms existing state-of-the-art techniques on single image human shape reconstruction by fully leveraging 1k-resolution input images. This provides context to an fine level which estimates highly detailed geometry by observing higher-resolution images. A coarse level observes the whole image at lower resolution and focuses on holistic reasoning. We address this limitation by formulating a multi-level architecture that is end-to-end trainable. 3.54K subscribers Subscribe 10K views 2 years ago Free 3D Human Models Free Blender Human Models Free 3D People Models Download here: There are. ![]() Due to memory limitations in current hardware, previous approaches tend to take low resolution images as input to cover large spatial context, and produce less precise (or low resolution) 3D estimates as a result. We argue that this limitation stems primarily form two conflicting requirements accurate predictions require large context, but precise predictions require high resolution. Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images. ![]() Recent advances in image-based 3D human shape estimation have been driven by the significant improvement in representation power afforded by deep neural networks. ![]()
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