372 citations · 409 across the 11 of their papers we have counts for
11 papers · 1 filter
Grounded Compositional and Diverse Text-to-3D with Pretrained Multi-View Diffusion Model
Xiaolong Li, Jiawei Mo, Ying Wang +7
In this paper, we propose an effective two-stage approach named Grounded-Dreamer to generate 3D assets that can accurately follow complex, compositional text prompts while achievin…
A Quantitative Evaluation of Score Distillation Sampling Based Text-to-3D
Xiaohan Fei, Chethan Parameshwara, Jiawei Mo +5
The development of generative models that create 3D content from a text prompt has made considerable strides thanks to the use of the score distillation sampling (SDS) method on pr…
Towards Visual Foundational Models of Physical Scenes
Chethan Parameshwara, Alessandro Achille, Matthew Trager +7
We describe a first step towards learning general-purpose visual representations of physical scenes using only image prediction as a training criterion. To do so, we first define "…
AIMusicGuru: Music Assisted Human Pose Correction
Snehesh Shrestha, Cornelia Fermüller, Tianyu Huang +4
Pose Estimation techniques rely on visual cues available through observations represented in the form of pixels. But the performance is bounded by the frame rate of the video and s…
DiffPoseNet: Direct Differentiable Camera Pose Estimation
Chethan M. Parameshwara, Gokul Hari, Cornelia Fermüller +2
Current deep neural network approaches for camera pose estimation rely on scene structure for 3D motion estimation, but this decreases the robustness and thereby makes cross-datase…
NudgeSeg: Zero-Shot Object Segmentation by Repeated Physical Interaction
Chahat Deep Singh, Nitin J. Sanket, Chethan M. Parameshwara +2
Recent advances in object segmentation have demonstrated that deep neural networks excel at object segmentation for specific classes in color and depth images. However, their perfo…
EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following
Nitin J. Sanket, Chahat Deep Singh, Chethan M. Parameshwara +3
The rapid rise of accessibility of unmanned aerial vehicles or drones pose a threat to general security and confidentiality. Most of the commercially available or custom-built dron…
SpikeMS: Deep Spiking Neural Network for Motion Segmentation
Chethan M. Parameshwara, Simin Li, Cornelia Fermüller +3
Spiking Neural Networks (SNN) are the so-called third generation of neural networks which attempt to more closely match the functioning of the biological brain. They inherently enc…
0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event Camera
Chethan M. Parameshwara, Nitin J. Sanket, Chahat Deep Singh +2
Segmentation of moving objects in dynamic scenes is a key process in scene understanding for navigation tasks. Classical cameras suffer from motion blur in such scenarios rendering…
Unsupervised Learning of Dense Optical Flow, Depth and Egomotion from Sparse Event Data
Chengxi Ye, Anton Mitrokhin, Cornelia Fermüller +2
In this work we present a lightweight, unsupervised learning pipeline for \textit{dense} depth, optical flow and egomotion estimation from sparse event output of the Dynamic Vision…
Event-based Moving Object Detection and Tracking
Anton Mitrokhin, Cornelia Fermuller, Chethan Parameshwara +1
Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), are ideally suited for real-time motion analysis. The unique properties encompassed in the readings of such sen…