Search Results for author:
Found 9 papers, 6 papers with code
EgoLifter: Open-world 3D Segmentation for Egocentric Perception
In this paper we present EgoLifter, a novel system that can automatically segment scenes captured from egocentric sensors into a complete decomposition of individual 3D objects.
Aria Everyday Activities Dataset
We present Aria Everyday Activities (AEA) Dataset, an egocentric multimodal open dataset recorded using Project Aria glasses.
ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning
We demonstrate the utility of this representation through a number of downstream planning tasks that are specified through abstract (language) prompts and require complex reasoning over spatial and semantic concepts.
Preserving Linear Separability in Continual Learning by Backward Feature Projection
To achieve a better stability-plasticity trade-off, we propose Backward Feature Projection (BFP), a method for continual learning that allows the new features to change up to a learnable linear transformation of the old features.
ConceptFusion: Open-set Multimodal 3D Mapping
ConceptFusion leverages the open-set capabilities of today's foundation models pre-trained on internet-scale data to reason about concepts across modalities such as natural language, images, and audio.
OSSID: Online Self-Supervised Instance Detection by (and for) Pose Estimation
In this paper, we propose the OSSID framework, leveraging a slow zero-shot pose estimator to self-supervise the training of a fast detection algorithm.
ZePHyR: Zero-shot Pose Hypothesis Rating
We also demonstrate how our system can be used by quickly scanning and building a model of a novel object, which can immediately be used by our method for pose estimation.
Deep Video Matting via Spatio-Temporal Alignment and Aggregation
Despite the significant progress made by deep learning in natural image matting, there has been so far no representative work on deep learning for video matting due to the inherent technical challenges in reasoning temporal domain and lack of large-scale video matting datasets.
LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup
Central to our method are multiple and overlapping local adversarial discriminators in a content-style disentangling network for achieving local detail transfer between facial images, with the use of asymmetric loss functions for dramatic makeup styles with high-frequency details.
