human-modeling
Papers with tag human-modeling
2022
- HDHumans: A Hybrid Approach for High-fidelity Digital HumansMarc Habermann, Lingjie Liu, Weipeng Xu, Gerard Pons-Moll, Michael Zollhoefer, and Christian TheobaltIn 2022
Photo-real digital human avatars are of enormous importance in graphics, asthey enable immersive communication over the globe, improve gaming andentertainment experiences, and can be particularly beneficial for AR and VRsettings. However, current avatar generation approaches either fall short inhigh-fidelity novel view synthesis, generalization to novel motions,reproduction of loose clothing, or they cannot render characters at the highresolution offered by modern displays. To this end, we propose HDHumans, whichis the first method for HD human character synthesis that jointly produces anaccurate and temporally coherent 3D deforming surface and highlyphoto-realistic images of arbitrary novel views and of motions not seen attraining time. At the technical core, our method tightly integrates a classicaldeforming character template with neural radiance fields (NeRF). Our method iscarefully designed to achieve a synergy between classical surface deformationand NeRF. First, the template guides the NeRF, which allows synthesizing novelviews of a highly dynamic and articulated character and even enables thesynthesis of novel motions. Second, we also leverage the dense pointcloudsresulting from NeRF to further improve the deforming surface via 3D-to-3Dsupervision. We outperform the state of the art quantitatively andqualitatively in terms of synthesis quality and resolution, as well as thequality of 3D surface reconstruction.
DeepCap的拓展
human-synthesis view-synthesis human-modeling human-performance-capture@inproceedings{HDHumans, title = {HDHumans: A Hybrid Approach for High-fidelity Digital Humans}, author = {Habermann, Marc and Liu, Lingjie and Xu, Weipeng and Pons-Moll, Gerard and Zollhoefer, Michael and Theobalt, Christian}, year = {2022}, tags = {human-synthesis, view-synthesis, human-modeling, human-performance-capture}, }