Search Results for author:
Found 7 papers, 6 papers with code
6D Object Pose Tracking in Internet Videos for Robotic Manipulation
Third, we thoroughly evaluate and ablate our 6D pose estimation method on YCB-V and HOPE-Video datasets as well as a new dataset of instructional videos manually annotated with approximate 6D object trajectories.
Ranked #1 on
6D Pose Estimation
on DTTD-Mobile
(AR CoU metric)
ShowHowTo: Generating Scene-Conditioned Step-by-Step Visual Instructions
The goal of this work is to generate step-by-step visual instructions in the form of a sequence of images, given an input image that provides the scene context and the sequence of textual instructions.
GenHowTo: Learning to Generate Actions and State Transformations from Instructional Videos
We address the task of generating temporally consistent and physically plausible images of actions and object state transformations.
Multi-Task Learning of Object State Changes from Uncurated Videos
We aim to learn to temporally localize object state changes and the corresponding state-modifying actions by observing people interacting with objects in long uncurated web videos.
Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos
In this paper, we seek to temporally localize object states (e. g. "empty" and "full" cup) together with the corresponding state-modifying actions ("pouring coffee") in long uncurated videos with minimal supervision.
TransNet V2: An effective deep network architecture for fast shot transition detection
Although automatic shot transition detection approaches are already investigated for more than two decades, an effective universal human-level model was not proposed yet.
Ranked #2 on
Camera shot boundary detection
on ClipShots
(using extra training data)
TransNet: A deep network for fast detection of common shot transitions
Shot boundary detection (SBD) is an important first step in many video processing applications.
