337 citations · 608 across the 32 of their papers we have counts for
5 papers · 1 filter
A Deep Recurrent Framework for Cleaning Motion Capture Data
Utkarsh Mall, G. Roshan Lal, Siddhartha Chaudhuri +1
We present a deep, bidirectional, recurrent framework for cleaning noisy and incomplete motion capture data. It exploits temporal coherence and joint correlations to infer adaptive…
ComplementMe: Weakly-Supervised Component Suggestions for 3D Modeling
Minhyuk Sung, Hao Su, Vladimir G. Kim +2
Assembly-based tools provide a powerful modeling paradigm for non-expert shape designers. However, choosing a component from a large shape repository and aligning it to a partial a…
Tags2Parts: Discovering Semantic Regions from Shape Tags
Sanjeev Muralikrishnan, Vladimir G. Kim, Siddhartha Chaudhuri
We propose a novel method for discovering shape regions that strongly correlate with user-prescribed tags. For example, given a collection of chairs tagged as either "has armrest"…
Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks
Haibin Huang, Evangelos Kalogerakis, Siddhartha Chaudhuri +3
We present a new local descriptor for 3D shapes, directly applicable to a wide range of shape analysis problems such as point correspondences, semantic segmentation, affordance pre…
GRASS: Generative Recursive Autoencoders for Shape Structures
Jun Li, Kai Xu, Siddhartha Chaudhuri +3
We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively character…