no code implementations • 9 Apr 2024 • Jane Wu, Georgios Pavlakos, Georgia Gkioxari, Jitendra Malik
At the same time, two strong anchors emerge in this setting: (1) estimated 3D hands help disambiguate the location and scale of the object, and (2) the set of manipulanda is small relative to all possible objects.
no code implementations • 6 Dec 2023 • Kira Prabhu, Jane Wu, Lynn Tsai, Peter Hedman, Dan B Goldman, Ben Poole, Michael Broxton
We compare our approach to 3D inpainting methods that focus on object removal.
no code implementations • 27 Nov 2023 • Jane Wu, Diego Thomas, Ronald Fedkiw
We present a novel deep learning-based approach to the 3D reconstruction of clothed humans using weak supervision via 2D normal maps.
no code implementations • 14 Mar 2023 • Tae Eun Choe, Jane Wu, Xiaolin Lin, Karen Kwon, Minwoo Park
We present an algorithm to detect unseen road debris using a small set of synthetic models.
no code implementations • 8 Jun 2020 • Jane Wu, Zhenglin Geng, Hui Zhou, Ronald Fedkiw
We present a novel learning framework for cloth deformation by embedding virtual cloth into a tetrahedral mesh that parametrizes the volumetric region of air surrounding the underlying body.
no code implementations • 20 Jan 2020 • Jane Wu, Yongxu Jin, Zhenglin Geng, Hui Zhou, Ronald Fedkiw
Regularization is used to avoid overfitting when training a neural network; unfortunately, this reduces the attainable level of detail hindering the ability to capture high-frequency information present in the training data.
no code implementations • 7 Dec 2018 • Michael Bao, Jane Wu, Xinwei Yao, Ronald Fedkiw
While much progress has been made in capturing high-quality facial performances using motion capture markers and shape-from-shading, high-end systems typically also rely on rotoscope curves hand-drawn on the image.