Browsing by Author "Manocha, Dinesh"

Now showing items 1-5 of 5

    • Efficient and Reliable Self‐Collision Culling Using Unprojected Normal Cones 

      Wang, Tongtong; Liu, Zhihua; Tang, Min; Tong, Ruofeng; Manocha, Dinesh (© 2017 The Eurographics Association and John Wiley & Sons Ltd., 2017)
      We present an efficient and accurate algorithm for self‐collision detection in deformable models. Our approach can perform discrete and continuous collision queries on triangulated meshes. We present a simple and linear ...

    • GPU-Based Contact-Aware Trajectory Optimization Using A Smooth Force Model 

      Pan, Zherong; Ren, Bo; Manocha, Dinesh (ACM, 2019)
      We present a new formulation of trajectory optimization for articulated bodies. Our approach uses a fully differentiable dynamic model of the articulated body, and a smooth force model that approximates all kinds of ...

    • HMLFC: Hierarchical Motion-Compensated Light Field Compression for Interactive Rendering 

      Pratapa, Srihari; Manocha, Dinesh (The Eurographics Association and John Wiley & Sons Ltd., 2019)
      We present a new motion-compensated hierarchical compression scheme (HMLFC) for encoding light field images (LFI) that is suitable for interactive rendering. Our method combines two different approaches, motion compensation ...

    • LPaintB: Learning to Paint from Self-Supervision 

      Jia, Biao; Brandt, Jonathan; Mech, Radomír; Kim, Byungmoon; Manocha, Dinesh (The Eurographics Association, 2019)
      We present a novel reinforcement learning-based natural media painting algorithm. Our goal is to reproduce a reference image using brush strokes and we encode the objective through observations. Our formulation takes into ...

    • N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks 

      Li, Yu Di; Tang, Min; Yang, Yun; Huang, Zi; Tong, Ruo Feng; Yang, Shuang Cai; Li, Yao; Manocha, Dinesh (The Eurographics Association and John Wiley & Sons Ltd., 2022)
      We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction. Our approach is general and can handle cloth or obstacles represented by triangle meshes with arbitrary topologies.We ...