6 papers
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…
ConsistentDreamer: View-Consistent Meshes Through Balanced Multi-View Gaussian Optimization
Onat Şahin, Mohammad Altillawi, George Eskandar +2
Recent advances in diffusion models have significantly improved 3D generation, enabling the use of assets generated from an image for embodied AI simulations. However, the one-to-m…