384 citations · 405 across the 5 of their papers we have counts for
6 papers · 1 filter
Self-supervised Neural Articulated Shape and Appearance Models
Fangyin Wei, Rohan Chabra, Lingni Ma +6
Learning geometry, motion, and appearance priors of object classes is important for the solution of a large variety of computer vision problems. While the majority of approaches ha…
LISA: Learning Implicit Shape and Appearance of Hands
Enric Corona, Tomas Hodan, Minh Vo +4
This paper proposes a do-it-all neural model of human hands, named LISA. The model can capture accurate hand shape and appearance, generalize to arbitrary hand subjects, provide de…
Identity-Disentangled Neural Deformation Model for Dynamic Meshes
Binbin Xu, Lingni Ma, Yuting Ye +3
Neural shape models can represent complex 3D shapes with a compact latent space. When applied to dynamically deforming shapes such as the human hands, however, they would need to p…
FroDO: From Detections to 3D Objects
Kejie Li, Martin Rünz, Meng Tang +8
Object-oriented maps are important for scene understanding since they jointly capture geometry and semantics, allow individual instantiation and meaningful reasoning about objects.…
The Replica Dataset: A Digital Replica of Indoor Spaces
Julian Straub, Thomas Whelan, Lingni Ma +27
We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale. Each scene consists of a dense mesh, high-resolution high-d…
Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform
Lingni Ma, Jörg Stückler, Tao Wu +1
Dense pixelwise prediction such as semantic segmentation is an up-to-date challenge for deep convolutional neural networks (CNNs). Many state-of-the-art approaches either tackle th…