Publications (6)
Towards Unstructured Unlabeled Optical Mocap: A Video Helps!
Nicholas Milef, John Keyser, Shu Kong
Optical motion capture (mocap) requires accurately reconstructing the human body from retroreflective markers, including pose and shape. In a typical mocap setting, marker labeling…
Indexing Analytics to Instances: How Integrating a Dashboard can Support Design Education
Ajit Jain, Andruid Kerne, Nic Lupfer +9
We investigate how to use AI-based analytics to support design education. The analytics at hand measure multiscale design, that is, students' use of space and scale to visually and…
On Optimal Sampling for Learning SDF Using MLPs Equipped with Positional Encoding
Guying Lin, Lei Yang, Yuan Liu +6
Neural implicit fields, such as the neural signed distance field (SDF) of a shape, have emerged as a powerful representation for many applications, e.g., encoding a 3D shape and pe…
Neural Parametric Surfaces for Shape Modeling
Lei Yang, Yongqing Liang, Xin Li +6
The recent surge of utilizing deep neural networks for geometric processing and shape modeling has opened up exciting avenues. However, there is a conspicuous lack of research effo…
Surface Extraction from Neural Unsigned Distance Fields
Congyi Zhang, Guying Lin, Lei Yang +5
We propose a method, named DualMesh-UDF, to extract a surface from unsigned distance functions (UDFs), encoded by neural networks, or neural UDFs. Neural UDFs are becoming increasi…
Patch-Grid: An Efficient and Feature-Preserving Neural Implicit Surface Representation
Guying Lin, Lei Yang, Congyi Zhang +6
Neural implicit representations are widely used for 3D shape modeling due to their smoothness and compactness, but traditional MLP-based methods struggle with sharp features, such…