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Found 7 papers, 0 papers with code
Reconstructing Hand-Held Objects in 3D
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.
Inpaint3D: 3D Scene Content Generation using 2D Inpainting Diffusion
We compare our approach to 3D inpainting methods that focus on object removal.
Weakly-Supervised 3D Reconstruction of Clothed Humans via Normal Maps
We present a novel deep learning-based approach to the 3D reconstruction of clothed humans using weak supervision via 2D normal maps.
HazardNet: Road Debris Detection by Augmentation of Synthetic Models
We present an algorithm to detect unseen road debris using a small set of synthetic models.
Skinning a Parameterization of Three-Dimensional Space for Neural Network Cloth
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.
Recovering Geometric Information with Learned Texture Perturbations
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.
Deep Energies for Estimating Three-Dimensional Facial Pose and Expression
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.
