3 citations · 8 across the 6 of their papers we have counts for
10 papers
MonoFusion: Sparse-View 4D Reconstruction via Monocular Fusion
Zihan Wang, Jeff Tan, Tarasha Khurana +2
We address the problem of dynamic scene reconstruction from sparse-view videos. Prior work often requires dense multi-view captures with hundreds of calibrated cameras (e.g. Panopt…
Reconstruct, Inpaint, Test-Time Finetune: Dynamic Novel-view Synthesis from Monocular Videos
Kaihua Chen, Tarasha Khurana, Deva Ramanan
We explore novel-view synthesis for dynamic scenes from monocular videos. Prior approaches rely on costly test-time optimization of 4D representations or do not preserve scene geom…
Using Diffusion Priors for Video Amodal Segmentation
Kaihua Chen, Deva Ramanan, Tarasha Khurana
Object permanence in humans is a fundamental cue that helps in understanding persistence of objects, even when they are fully occluded in the scene. Present day methods in object s…
Predicting Long-horizon Futures by Conditioning on Geometry and Time
Tarasha Khurana, Deva Ramanan
Our work explores the task of generating future sensor observations conditioned on the past. We are motivated by `predictive coding' concepts from neuroscience as well as robotic a…
TAO-Amodal: A Benchmark for Tracking Any Object Amodally
Cheng-Yen Hsieh, Kaihua Chen, Achal Dave +2
Amodal perception, the ability to comprehend complete object structures from partial visibility, is a fundamental skill, even for infants. Its significance extends to applications…
Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting
Tarasha Khurana, Peiyun Hu, David Held +1
Predicting how the world can evolve in the future is crucial for motion planning in autonomous systems. Classical methods are limited because they rely on costly human annotations…
Differentiable Raycasting for Self-supervised Occupancy Forecasting
Tarasha Khurana, Peiyun Hu, Achal Dave +3
Motion planning for safe autonomous driving requires learning how the environment around an ego-vehicle evolves with time. Ego-centric perception of driveable regions in a scene no…
BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video
Ali Athar, Jonathon Luiten, Paul Voigtlaender +4
Multiple existing benchmarks involve tracking and segmenting objects in video e.g., Video Object Segmentation (VOS) and Multi-Object Tracking and Segmentation (MOTS), but there is…
Detecting Invisible People
Tarasha Khurana, Achal Dave, Deva Ramanan
Monocular object detection and tracking have improved drastically in recent years, but rely on a key assumption: that objects are visible to the camera. Many offline tracking appro…
TAO: A Large-Scale Benchmark for Tracking Any Object
Achal Dave, Tarasha Khurana, Pavel Tokmakov +2
For many years, multi-object tracking benchmarks have focused on a handful of categories. Motivated primarily by surveillance and self-driving applications, these datasets provide…