254 citations · 1.4k across the 100 of their papers we have counts for
18 papers · 1 filter
Self-Supervised Learning of Action Affordances as Interaction Modes
Liquan Wang, Nikita Dvornik, Rafael Dubeau +2
When humans perform a task with an articulated object, they interact with the object only in a handful of ways, while the space of all possible interactions is nearly endless. This…
SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models
Ziyi Wu, Jingyu Hu, Wuyue Lu +2
Object-centric learning aims to represent visual data with a set of object entities (a.k.a. slots), providing structured representations that enable systematic generalization. Leve…
StepFormer: Self-supervised Step Discovery and Localization in Instructional Videos
Nikita Dvornik, Isma Hadji, Ran Zhang +4
Instructional videos are an important resource to learn procedural tasks from human demonstrations. However, the instruction steps in such videos are typically short and sparse, wi…
-Patching: A Framework for Rapid Adaptation of Pre-trained Convolutional Networks without Base Performance Loss
Chaitanya Devaguptapu, Samarth Sinha, K J Joseph +2
Models pre-trained on large-scale datasets are often fine-tuned to support newer tasks and datasets that arrive over time. This process necessitates storing copies of the model ove…
nerf2nerf: Pairwise Registration of Neural Radiance Fields
Lily Goli, Daniel Rebain, Sara Sabour +2
We introduce a technique for pairwise registration of neural fields that extends classical optimization-based local registration (i.e. ICP) to operate on Neural Radiance Fields (Ne…
Breaking Bad: A Dataset for Geometric Fracture and Reassembly
Silvia Sellán, Yun-Chun Chen, Ziyi Wu +2
We introduce Breaking Bad, a large-scale dataset of fractured objects. Our dataset consists of over one million fractured objects simulated from ten thousand base models. The fract…
SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models
Ziyi Wu, Nikita Dvornik, Klaus Greff +2
Understanding dynamics from visual observations is a challenging problem that requires disentangling individual objects from the scene and learning their interactions. While recent…
Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors
Yun-Chun Chen, Haoda Li, Dylan Turpin +2
Learning to autonomously assemble shapes is a crucial skill for many robotic applications. While the majority of existing part assembly methods focus on correctly posing semantic p…
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval
Satya Krishna Gorti, Noel Vouitsis, Junwei Ma +4
In text-video retrieval, the objective is to learn a cross-modal similarity function between a text and a video that ranks relevant text-video pairs higher than irrelevant pairs. H…
Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects
Haoping Xu, Yi Ru Wang, Sagi Eppel +3
The basis of many object manipulation algorithms is RGB-D input. Yet, commodity RGB-D sensors can only provide distorted depth maps for a wide range of transparent objects due ligh…
Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers
Nikita Dvornik, Isma Hadji, Konstantinos G. Derpanis +2
In this work, we consider the problem of sequence-to-sequence alignment for signals containing outliers. Assuming the absence of outliers, the standard Dynamic Time Warping (DTW) a…
Modular Action Concept Grounding in Semantic Video Prediction
Wei Yu, Wenxin Chen, Songhenh Yin +2
Recent works in video prediction have mainly focused on passive forecasting and low-level action-conditional prediction, which sidesteps the learning of interaction between agents…
Semi-Supervised StyleGAN for Disentanglement Learning
Weili Nie, Tero Karras, Animesh Garg +4
Disentanglement learning is crucial for obtaining disentangled representations and controllable generation. Current disentanglement methods face several inherent limitations: diffi…
Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos
Aysegul Dundar, Kevin J. Shih, Animesh Garg +3
Unsupervised landmark learning is the task of learning semantic keypoint-like representations without the use of expensive input keypoint-level annotations. A popular approach is t…
Video Interpolation and Prediction with Unsupervised Landmarks
Kevin J. Shih, Aysegul Dundar, Animesh Garg +3
Prediction and interpolation for long-range video data involves the complex task of modeling motion trajectories for each visible object, occlusions and dis-occlusions, as well as…
Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration
De-An Huang, Suraj Nair, Danfei Xu +5
Our goal is to generate a policy to complete an unseen task given just a single video demonstration of the task in a given domain. We hypothesize that to successfully generalize to…
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image
Andrey Kurenkov, Jingwei Ji, Animesh Garg +4
3D reconstruction from a single image is a key problem in multiple applications ranging from robotic manipulation to augmented reality. Prior methods have tackled this problem thro…
Weakly supervised 3D Reconstruction with Adversarial Constraint
JunYoung Gwak, Christopher B. Choy, Animesh Garg +2
Supervised 3D reconstruction has witnessed a significant progress through the use of deep neural networks. However, this increase in performance requires large scale annotations of…