18 citations · 20 across the 7 of their papers we have counts for
7 papers
NeuVAS: Neural Implicit Surfaces for Variational Shape Modeling
Pengfei Wang, Qiujie Dong, Fangtian Liang +11
Neural implicit shape representation has drawn significant attention in recent years due to its smoothness, differentiability, and topological flexibility. However, directly modeli…
Internal State Estimation in Groups via Active Information Gathering
Xuebo Ji, Zherong Pan, Xifeng Gao +7
Accurately estimating human internal states, such as personality traits or behavioral patterns, is critical for enhancing the effectiveness of human-robot interaction, particularly…
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…
Generative Hierarchical Temporal Transformer for Hand Pose and Action Modeling
Yilin Wen, Hao Pan, Takehiko Ohkawa +5
We present a novel unified framework that concurrently tackles recognition and future prediction for human hand pose and action modeling. Previous works generally provide isolated…
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…
DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation
Yilin Wen, Xiangyu Li, Hao Pan +4
Scalable 6D pose estimation for rigid objects from RGB images aims at handling multiple objects and generalizing to novel objects. Building on a well-known auto-encoding framework…