61 research outputs found
Fast Inference and Update of Probabilistic Density Estimation on Trajectory Prediction
Safety-critical applications such as autonomous vehicles and social robots
require fast computation and accurate probability density estimation on
trajectory prediction. To address both requirements, this paper presents a new
normalizing flow-based trajectory prediction model named FlowChain. FlowChain
is a stack of conditional continuously-indexed flows (CIFs) that are expressive
and allow analytical probability density computation. This analytical
computation is faster than the generative models that need additional
approximations such as kernel density estimation. Moreover, FlowChain is more
accurate than the Gaussian mixture-based models due to fewer assumptions on the
estimated density. FlowChain also allows a rapid update of estimated
probability densities. This update is achieved by adopting the \textit{newest
observed position} and reusing the flow transformations and its
log-det-jacobians that represent the \textit{motion trend}. This update is
completed in less than one millisecond because this reuse greatly omits the
computational cost. Experimental results showed our FlowChain achieved
state-of-the-art trajectory prediction accuracy compared to previous methods.
Furthermore, our FlowChain demonstrated superiority in the accuracy and speed
of density estimation. Our code is available at
\url{https://github.com/meaten/FlowChain-ICCV2023}Comment: Accepted at ICCV202
MotionAug: Augmentation with Physical Correction for Human Motion Prediction
This paper presents a motion data augmentation scheme incorporating motion
synthesis encouraging diversity and motion correction imposing physical
plausibility. This motion synthesis consists of our modified Variational
AutoEncoder (VAE) and Inverse Kinematics (IK). In this VAE, our proposed
sampling-near-samples method generates various valid motions even with
insufficient training motion data. Our IK-based motion synthesis method allows
us to generate a variety of motions semi-automatically. Since these two schemes
generate unrealistic artifacts in the synthesized motions, our motion
correction rectifies them. This motion correction scheme consists of imitation
learning with physics simulation and subsequent motion debiasing. For this
imitation learning, we propose the PD-residual force that significantly
accelerates the training process. Furthermore, our motion debiasing
successfully offsets the motion bias induced by imitation learning to maximize
the effect of augmentation. As a result, our method outperforms previous
noise-based motion augmentation methods by a large margin on both Recurrent
Neural Network-based and Graph Convolutional Network-based human motion
prediction models. The code is available at
https://github.com/meaten/MotionAug.Comment: Accepted at CVPR202
Kernelized Back-Projection Networks for Blind Super Resolution
Since non-blind Super Resolution (SR) fails to super-resolve Low-Resolution
(LR) images degraded by arbitrary degradations, SR with the degradation model
is required. However, this paper reveals that non-blind SR that is trained
simply with various blur kernels exhibits comparable performance as those with
the degradation model for blind SR. This result motivates us to revisit
high-performance non-blind SR and extend it to blind SR with blur kernels. This
paper proposes two SR networks by integrating kernel estimation and SR branches
in an iterative end-to-end manner. In the first model, which is called the
Kernel Conditioned Back-Projection Network (KCBPN), the low-dimensional kernel
representations are estimated for conditioning the SR branch. In our second
model, the Kernelized BackProjection Network (KBPN), a raw kernel is estimated
and directly employed for modeling the image degradation. The estimated kernel
is employed not only for back-propagating its residual but also for
forward-propagating the residual to iterative stages. This forward-propagation
encourages these stages to learn a variety of different features in different
stages by focusing on pixels with large residuals in each stage. Experimental
results validate the effectiveness of our proposed networks for kernel
estimation and SR. We will release the code for this work.Comment: The first two authors contributed equally to this wor
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