4 citations · 7 across the 7 of their papers we have counts for
7 papers
CacheFlow: Fast Human Motion Prediction by Cached Normalizing Flow
Takahiro Maeda, Jinkun Cao, Norimichi Ukita +1
Many density estimation techniques for 3D human motion prediction require a significant amount of inference time, often exceeding the duration of the predicted time horizon. To add…
Physical Plausibility-aware Trajectory Prediction via Locomotion Embodiment
Hiromu Taketsugu, Takeru Oba, Takahiro Maeda +2
Humans can predict future human trajectories even from momentary observations by using human pose-related cues. However, previous Human Trajectory Prediction (HTP) methods leverage…
Multimodal Active Measurement for Human Mesh Recovery in Close Proximity
Takahiro Maeda, Keisuke Takeshita, Norimichi Ukita +1
For physical human-robot interactions (pHRI), a robot needs to estimate the accurate body pose of a target person. However, in these pHRI scenarios, the robot cannot fully observe…
Fast Inference and Update of Probabilistic Density Estimation on Trajectory Prediction
Takahiro Maeda, Norimichi Ukita
Safety-critical applications such as autonomous vehicles and social robots require fast computation and accurate probability density estimation on trajectory prediction. To address…
Kernelized Back-Projection Networks for Blind Super Resolution
Tomoki Yoshida, Yuki Kondo, Takahiro Maeda +2
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…
MotionAug: Augmentation with Physical Correction for Human Motion Prediction
Takahiro Maeda, Norimichi Ukita
This paper presents a motion data augmentation scheme incorporating motion synthesis encouraging diversity and motion correction imposing physical plausibility. This motion synthes…
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
Goutam Bhat, Martin Danelljan, Radu Timofte +25
This paper reviews the NTIRE2021 challenge on burst super-resolution. Given a RAW noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times hig…