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HumanRAM: Feed-forward Human Reconstruction and Animation Model using Transformers

DescriptionExisting avatar methods typically require sophisticated dense-view capture and/or time-consuming per-subject optimization processes. HumanRAM proposes a feed-forward approach for generalizable human reconstruction and animation from monocular or sparse human images. Experiments show that HumanRAM achieves state-of-the-art results in terms of reconstruction accuracy, animation fidelity, and generalization performance.

Authors

Zhiyuan Yu

Department of Mathematics, Hong Kong University of Science and Technology

Zhe Li

Huawei

Hujun Bao

State Key Laboratory of CAD&CG, Zhejiang University

Can Yang

Department of Mathematics, Hong Kong University of Science and Technology