1 citations · 1 across the 10 of their papers we have counts for
11 papers
SNM-VFI: Symmetric Nonlinear Motion-Guided Generative Video Frame Interpolation
Jisoo Jeong, Hong Cai, Jamie Menjay Lin +5
We propose Symmetric Nonlinear Motion-guided Generative Video Frame Interpolation (SNM-VFI), a training-free framework for motion-controllable generative video frame interpolation…
CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving
Yunxiao Shi, Hong Cai, Mohammad Ghavamzadeh +1
End-to-end autonomous driving (E2E-AD) aims to directly map raw sensor information to driving actions. Recently, with the rapid advancement of multi-modal large language models (ML…
MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving
Rajeev Yasarla, Deepti Hegde, Hsin-Pai Cheng +9
Vision-language-action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings due to being trained under traditional im…
Generative Scenario Rollouts for End-to-End Autonomous Driving
Rajeev Yasarla, Deepti Hegde, Shizhong Han +10
Vision-Language-Action (VLA) models are emerging as highly effective planning models for end-to-end autonomous driving systems. However, current works mostly rely on imitation lear…
ODG: Occupancy Prediction Using Dual Gaussians
Yunxiao Shi, Yinhao Zhu, Shizhong Han +4
Occupancy prediction infers fine-grained 3D geometry and semantics from camera images of the surrounding environment, making it a critical perception task for autonomous driving. E…
RoCA: Robust Cross-Domain End-to-End Autonomous Driving
Rajeev Yasarla, Shizhong Han, Hsin-Pai Cheng +7
End-to-end (E2E) autonomous driving has recently emerged as a new paradigm, offering significant potential. However, few studies have looked into the practical challenge of deploym…
BePo: Dual Representation for 3D Occupancy Prediction
Yunxiao Shi, Hong Cai, Jisoo Jeong +4
3D occupancy infers fine-grained 3D geometry and semantics which is critical for autonomous driving. Most existing approaches carry high compute costs, requiring dense 3D feature v…
H3O: Hyper-Efficient 3D Occupancy Prediction with Heterogeneous Supervision
Yunxiao Shi, Hong Cai, Amin Ansari +1
3D occupancy prediction has recently emerged as a new paradigm for holistic 3D scene understanding and provides valuable information for downstream planning in autonomous driving.…
PADRe: A Unifying Polynomial Attention Drop-in Replacement for Efficient Vision Transformer
Pierre-David Letourneau, Manish Kumar Singh, Hsin-Pai Cheng +6
We present Polynomial Attention Drop-in Replacement (PADRe), a novel and unifying framework designed to replace the conventional self-attention mechanism in transformer models. Not…
DeCoTR: Enhancing Depth Completion with 2D and 3D Attentions
Yunxiao Shi, Manish Kumar Singh, Hong Cai +1
In this paper, we introduce a novel approach that harnesses both 2D and 3D attentions to enable highly accurate depth completion without requiring iterative spatial propagations. S…
FutureDepth: Learning to Predict the Future Improves Video Depth Estimation
Rajeev Yasarla, Manish Kumar Singh, Hong Cai +6
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimati…