98 citations · 851 across the 68 of their papers we have counts for
8 papers · 1 filter
Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation
Anthony Simeonov, Yilun Du, Andrea Tagliasacchi +4
We present Neural Descriptor Fields (NDFs), an object representation that encodes both points and relative poses between an object and a target (such as a robot gripper or a rack u…
Learning to Compose Visual Relations
Nan Liu, Shuang Li, Yilun Du +2
The visual world around us can be described as a structured set of objects and their associated relations. An image of a room may be conjured given only the description of the unde…
Learning Signal-Agnostic Manifolds of Neural Fields
Yilun Du, Katherine M. Collins, Joshua B. Tenenbaum +1
Deep neural networks have been used widely to learn the latent structure of datasets, across modalities such as images, shapes, and audio signals. However, existing models are gene…
Unsupervised Learning of Compositional Energy Concepts
Yilun Du, Shuang Li, Yash Sharma +2
Humans are able to rapidly understand scenes by utilizing concepts extracted from prior experience. Such concepts are diverse, and include global scene descriptors, such as the wea…
The Neural MMO Platform for Massively Multiagent Research
Joseph Suarez, Yilun Du, Clare Zhu +2
Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular game systems. Existing environ…
Weakly Supervised Human-Object Interaction Detection in Video via Contrastive Spatiotemporal Regions
Shuang Li, Yilun Du, Antonio Torralba +2
We introduce the task of weakly supervised learning for detecting human and object interactions in videos. Our task poses unique challenges as a system does not know what types of…
Curious Representation Learning for Embodied Intelligence
Yilun Du, Chuang Gan, Phillip Isola
Self-supervised representation learning has achieved remarkable success in recent years. By subverting the need for supervised labels, such approaches are able to utilize the numer…
3D Shape Generation and Completion through Point-Voxel Diffusion
Linqi Zhou, Yilun Du, Jiajun Wu
We propose a novel approach for probabilistic generative modeling of 3D shapes. Unlike most existing models that learn to deterministically translate a latent vector to a shape, ou…