Search Results for author:
Found 6 papers, 0 papers with code
FlowIBR: Leveraging Pre-Training for Efficient Neural Image-Based Rendering of Dynamic Scenes
We introduce FlowIBR, a novel approach for efficient monocular novel view synthesis of dynamic scenes.
Adjustable Visual Appearance for Generalizable Novel View Synthesis
We present a generalizable novel view synthesis method which enables modifying the visual appearance of an observed scene so rendered views match a target weather or lighting condition without any scene specific training or access to reference views at the target condition.
Embodied Learning for Lifelong Visual Perception
As we study this task in a lifelong learning context, the agents should use knowledge gained in earlier visited environments in order to guide their exploration and active learning strategy in successively visited buildings.
Embodied Visual Active Learning for Semantic Segmentation
We study the task of embodied visual active learning, where an agent is set to explore a 3d environment with the goal to acquire visual scene understanding by actively selecting views for which to request annotation.
Semantic Video Segmentation by Gated Recurrent Flow Propagation
In this paper we present a deep, end-to-end trainable methodology to video segmentation that is capable of leveraging information present in unlabeled data in order to improve semantic estimates.
Ranked #7 on
Video Semantic Segmentation
on Cityscapes val
