Stanford Advisors

Current Research and Scholarly Interests

character animation, physics-based character control, crowd simulation

All Publications

  • AdaptNet: Policy Adaptation for Physics-Based Character Control ACM TRANSACTIONS ON GRAPHICS Xu, P., Xie, K., Andrews, S., Kry, P. G., Neff, M., McGuire, M., Karamouzas, I., Zordan, V. 2023; 42 (6)

    View details for DOI 10.1145/3618375

    View details for Web of Science ID 001139790400005

  • Mask R-CNN provides efficient and accurate measurement of chondrocyte viability in the label-free assessment of articular cartilage. Osteoarthritis and cartilage open Fan, H., Xu, P., Chen, X., Li, Y., Zhang, Z., Hsu, J., Le, M., Ye, E., Gao, B., Demos, H., Yao, H., Ye, T. 2023; 5 (4): 100415


    Chondrocyte viability (CV) can be measured with the label-free method using second harmonic generation (SHG) and two-photon excitation autofluorescence (TPAF) imaging. To automate the image processing for the label-free CV measurement, we previously demonstrated a two-step deep-learning method: Step 1 used a U-Net to segment the lacuna area on SHG images; Step 2 used dual CNN networks to count live cells and the total number of cells in extracted cell clusters from TPAF images. This study aims to develop one-step deep learning methods to improve the efficiency of CV measurement.TPAF/SHG images were acquired simultaneously on cartilage samples from rats and pigs using two-photon microscopes and were merged to form RGB color images with red, green, and blue channels assigned to emission bands of oxidized flavoproteins, reduced forms of nicotinamide adenine dinucleotide, and SHG signals, respectively. Based on the Mask R-CNN, we designed a deep learning network and its denoising version using Wiener deconvolution for CV measurement.Using training and test datasets from rat and porcine cartilage, we have demonstrated that Mask R-CNN-based networks can segment and classify individual cells with a single-step processing flow. The absolute error (difference between the measured and the ground-truth CV) of the CV measurement using the Mask R-CNN with or without Wiener deconvolution denoising reaches 0.01 or 0.08, respectively; the error of the previous CV networks is 0.18, significantly larger than that of the Mask R-CNN methods.Mask R-CNN-based deep-learning networks improve efficiency and accuracy of the label-free CV measurement.

    View details for DOI 10.1016/j.ocarto.2023.100415

    View details for PubMedID 38025155

    View details for PubMedCentralID PMC10679817

  • Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction IEEE ROBOTICS AND AUTOMATION LETTERS Xu, P., Hayet, J., Karamouzas, I. 2023; 8 (9): 5440-5447
  • Composite Motion Learning with Task Control Xu, P., Shang, X., Zordan, V., Karamouzas, I. ASSOC COMPUTING MACHINERY. 2023

    View details for DOI 10.1145/3592447

    View details for Web of Science ID 001044671300059

  • Too Stiff, Too Strong, Too Smart: Evaluating Fundamental Problems with Motion Control Policies PROCEEDINGS OF THE ACM ON COMPUTER GRAPHICS AND INTERACTIVE TECHNIQUES Xie, K., Xu, P., Andrews, S., Zordan, V. B., Kry, P. G. 2023; 6 (3)

    View details for DOI 10.1145/3606935

    View details for Web of Science ID 001059100600017

  • SocialVAE: Human Trajectory Prediction Using Timewise Latents Xu, P., Hayet, J., Karamouzas, I., Avidan, S., Brostow, G., Cisse, M., Farinella, G. M., Hassner, T. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 511-528
  • Automated chondrocyte viability analysis of articular cartilage based on deep learning segmentation and classification of two-photon microscopic images Fan, H., Xu, P., Le, M., Hsu, J., Chen, X., Li, Y., Zhang, Z., Gao, B., Woolf, S., Ye, T., Brown, T. G., Wilson, T., Waller, L. SPIE-INT SOC OPTICAL ENGINEERING. 2022

    View details for DOI 10.1117/12.2609880

    View details for Web of Science ID 000831998600011

  • A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Control PROCEEDINGS OF THE ACM ON COMPUTER GRAPHICS AND INTERACTIVE TECHNIQUES Xu, P., Karamouzas, I. 2021; 4 (3)

    View details for DOI 10.1145/3480148

    View details for Web of Science ID 000703447500014

  • Human-Inspired Multi-Agent Navigation using Knowledge Distillation Xu, P., Karamouzas, I., IEEE IEEE. 2021: 8105-8112