1 citations · 2 across the 13 of their papers we have counts for
15 papers
VidAct: Learning Manipulation from In-the-Wild Videos with Object-Centric 3D Awareness
Hang Li, Mingxin Zhang, Zihan Wu +9
Video demonstrations offer a scalable alternative to costly robot data for learning manipulation, yet existing reconstruction-based approaches often rely on constrained camera view…
VideoWeaver: Multimodal Multi-View Video-to-Video Transfer for Embodied Agents
George Eskandar, Fengyi Shen, Mohammad Altillawi +4
Recent progress in video-to-video (V2V) translation has enabled realistic resimulation of embodied AI demonstrations, a capability that allows pretrained robot policies to be trans…
ReMem-VLA: Empowering Vision-Language-Action Model with Memory via Dual-Level Recurrent Queries
Hang Li, Fengyi Shen, Dong Chen +6
Vision-language-action (VLA) models for closed-loop robot control are typically cast under the Markov assumption, making them prone to errors on tasks requiring historical context.…
ConfCtrl: Enabling Precise Camera Control in Video Diffusion via Confidence-Aware Interpolation
Liudi Yang, George Eskandar, Fengyi Shen +5
We address the challenge of novel view synthesis from only two input images under large viewpoint changes. Existing regression-based methods lack the capacity to reconstruct unseen…
CoVAR: Co-generation of Video and Action for Robotic Manipulation via Multi-Modal Diffusion
Liudi Yang, Yang Bai, George Eskandar +5
We present a method to generate video-action pairs that follow text instructions, starting from an initial image observation and the robot's joint states. Our approach automaticall…
DRAW2ACT: Turning Depth-Encoded Trajectories into Robotic Demonstration Videos
Yang Bai, Liudi Yang, George Eskandar +4
Video diffusion models provide powerful real-world simulators for embodied AI but remain limited in controllability for robotic manipulation. Recent works on trajectory-conditioned…
RoboEnvision: A Long-Horizon Video Generation Model for Multi-Task Robot Manipulation
Liudi Yang, Yang Bai, George Eskandar +7
We address the problem of generating long-horizon videos for robotic manipulation tasks. Text-to-video diffusion models have made significant progress in photorealism, language und…
RoboSwap: A GAN-driven Video Diffusion Framework For Unsupervised Robot Arm Swapping
Yang Bai, Liudi Yang, George Eskandar +5
Recent advancements in generative models have revolutionized video synthesis and editing. However, the scarcity of diverse, high-quality datasets continues to hinder video-conditio…
CE-NPBG: Connectivity Enhanced Neural Point-Based Graphics for Novel View Synthesis in Autonomous Driving Scenes
Mohammad Altillawi, Fengyi Shen, Liudi Yang +2
Current point-based approaches encounter limitations in scalability and rendering quality when using large 3D point cloud maps because using them directly for novel view synthesis…
ControlUDA: Controllable Diffusion-assisted Unsupervised Domain Adaptation for Cross-Weather Semantic Segmentation
Fengyi Shen, Li Zhou, Kagan Kucukaytekin +3
Data generation is recognized as a potent strategy for unsupervised domain adaptation (UDA) pertaining semantic segmentation in adverse weathers. Nevertheless, these adverse weathe…
Long-Term Invariant Local Features via Implicit Cross-Domain Correspondences
Zador Pataki, Mohammad Altillawi, Menelaos Kanakis +5
Modern learning-based visual feature extraction networks perform well in intra-domain localization, however, their performance significantly declines when image pairs are captured…
DiGA: Distil to Generalize and then Adapt for Domain Adaptive Semantic Segmentation
Fengyi Shen, Akhil Gurram, Ziyuan Liu +2
Domain adaptive semantic segmentation methods commonly utilize stage-wise training, consisting of a warm-up and a self-training stage. However, this popular approach still faces se…
LoopDA: Constructing Self-loops to Adapt Nighttime Semantic Segmentation
Fengyi Shen, Zador Pataki, Akhil Gurram +3
Due to the lack of training labels and the difficulty of annotating, dealing with adverse driving conditions such as nighttime has posed a huge challenge to the perception system o…
TridentAdapt: Learning Domain-invariance via Source-Target Confrontation and Self-induced Cross-domain Augmentation
Fengyi Shen, Akhil Gurram, Ahmet Faruk Tuna +2
Due to the difficulty of obtaining ground-truth labels, learning from virtual-world datasets is of great interest for real-world applications like semantic segmentation. From domai…
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision
Akhil Gurram, Ahmet Faruk Tuna, Fengyi Shen +2
Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearanc…