EG 2021 - Short Papers

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https://diglib.eg.org/handle/10.2312/2633005

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Browsing EG 2021 - Short Papers by Subject "Neural networks"

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    Auto-rigging 3D Bipedal Characters in Arbitrary Poses
    (The Eurographics Association, 2021) Kim, Jeonghwan; Son, Hyeontae; Bae, Jinseok; Kim, Young Min; Theisel, Holger and Wimmer, Michael
    We present an end-to-end algorithm that can automatically rig a given 3D character such that it is ready for 3D animation. The animation of a virtual character requires the skeletal motion defined with bones and joints, and the corresponding deformation of the mesh represented with skin weights. While the conventional animation pipeline requires the initial 3D character to be in the predefined default pose, our pipeline can rig a 3D character in arbitrary pose. We handle the increased ambiguity by fixing the skeletal topology and solving for the full deformation space. After the skeletal positions and orientations are fully discovered, we can deform the provided 3D character into the default pose, from which we can animate the character with the help of recent motion-retargeting techniques. Our results show that we can successfully animate initially deformed characters, which was not possible with previous works.
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    Data-driven Garment Pattern Estimation from 3D Geometries
    (The Eurographics Association, 2021) Goto, Chihiro; Umetani, Nobuyuki; Theisel, Holger and Wimmer, Michael
    Three-dimensional scanning technology recently becomes widely available to the public. However, it is difficult to simulate clothing deformation from the scanned people because scanned data lacks information required for the clothing simulation. In this paper, we present a technique to estimate clothing patterns from a scanned person in cloth. Our technique uses image-based deep learning to estimate the type of pattern on the projected image. The key contribution is converting image-based inference into three-dimensional clothing pattern estimation. We evaluate our technique by applying our technique to an actual scan.