VMV2023

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https://diglib.eg.org/handle/10.2312/3543878

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Browsing VMV2023 by Subject "Animation"

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    Video-Driven Animation of Neural Head Avatars
    (The Eurographics Association, 2023) Paier, Wolfgang; Hinzer, Paul; Hilsmann, Anna; Eisert, Peter; Guthe, Michael; Grosch, Thorsten
    We present a new approach for video-driven animation of high-quality neural 3D head models, addressing the challenge of person-independent animation from video input. Typically, high-quality generative models are learned for specific individuals from multi-view video footage, resulting in person-specific latent representations that drive the generation process. In order to achieve person-independent animation from video input, we introduce an LSTM-based animation network capable of translating person-independent expression features into personalized animation parameters of person-specific 3D head models. Our approach combines the advantages of personalized head models (high quality and realism) with the convenience of video-driven animation employing multi-person facial performance capture.We demonstrate the effectiveness of our approach on synthesized animations with high quality based on different source videos as well as an ablation study.
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    Weighted Laplacian Smoothing for Surface Reconstruction of Particle-based Fluids
    (The Eurographics Association, 2023) Löschner, Fabian; Böttcher, Timna; Rhys Jeske, Stefan; Bender, Jan; Guthe, Michael; Grosch, Thorsten
    In physically-based animation, producing detailed and realistic surface reconstructions for rendering is an important part of a simulation pipeline for particle-based fluids. In this paper we propose a post-processing approach to obtain smooth surfaces from ''blobby'' marching cubes triangulations without visual volume loss or shrinkage of drops and splashes. In contrast to other state-of-the-art methods that often require changes to the entire reconstruction pipeline our approach is easy to implement and less computationally expensive. The main component is Laplacian mesh smoothing with our proposed feature weights that dampen the smoothing in regions of the mesh with splashes and isolated particles without reducing effectiveness in regions that are supposed to be flat. In addition, we suggest a specialized decimation procedure to avoid artifacts due to low-quality triangle configurations generated by marching cubes and a normal smoothing pass to further increase quality when visualizing the mesh with physically-based rendering. For improved computational efficiency of the method, we outline the option of integrating computation of our weights into an existing reconstruction pipeline as most involved quantities are already known during reconstruction. Finally, we evaluate our post-processing implementation on high-resolution smoothed particle hydrodynamics (SPH) simulations.