4 citations · 9 across the 13 of their papers we have counts for
14 papers
Ground Reaction Inertial Poser: Physics-based Human Motion Capture from Sparse IMUs and Insole Pressure Sensors
Ryosuke Hori, Jyun-Ting Song, Zhengyi Luo +4
We propose Ground Reaction Inertial Poser (GRIP), a method that reconstructs physically plausible human motion using four wearable devices. Unlike conventional IMU-only approaches,…
SAM 3D Body: Robust Full-Body Human Mesh Recovery
Xitong Yang, Devansh Kukreja, Don Pinkus +11
We introduce SAM 3D Body (3DB), a promptable model for single-image full-body 3D human mesh recovery (HMR) that demonstrates state-of-the-art performance, with strong generalizatio…
WonderZoom: Multi-Scale 3D World Generation
Jin Cao, Hong-Xing Yu, Jiajun Wu
We present WonderZoom, a novel approach to generating 3D scenes with contents across multiple spatial scales from a single image. Existing 3D world generation models remain limited…
GENMO: A GENeralist Model for Human MOtion
Jiefeng Li, Jinkun Cao, Haotian Zhang +4
Human motion modeling traditionally separates motion generation and estimation into distinct tasks with specialized models. Motion generation models focus on creating diverse, real…
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…
Harmony4D: A Video Dataset for In-The-Wild Close Human Interactions
Rawal Khirodkar, Jyun-Ting Song, Jinkun Cao +2
Understanding how humans interact with each other is key to building realistic multi-human virtual reality systems. This area remains relatively unexplored due to the lack of large…
Joint Diffusion for Universal Hand-Object Grasp Generation
Jinkun Cao, Jingyuan Liu, Kris Kitani +1
Predicting and generating human hand grasp over objects is critical for animation and robotic tasks. In this work, we focus on generating both the hand and objects in a grasp by a…
Omnigrasp: Grasping Diverse Objects with Simulated Humanoids
Zhengyi Luo, Jinkun Cao, Sammy Christen +3
We present a method for controlling a simulated humanoid to grasp an object and move it to follow an object's trajectory. Due to the challenges in controlling a humanoid with dexte…
SMPLOlympics: Sports Environments for Physically Simulated Humanoids
Zhengyi Luo, Jiashun Wang, Kangni Liu +9
We present SMPLOlympics, a collection of physically simulated environments that allow humanoids to compete in a variety of Olympic sports. Sports simulation offers a rich and stand…
CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics
Jiawei Gao, Ziqin Wang, Zeqi Xiao +7
Enabling humanoid robots to clean rooms has long been a pursued dream within humanoid research communities. However, many tasks require multi-humanoid collaboration, such as carryi…
Real-Time Simulated Avatar from Head-Mounted Sensors
Zhengyi Luo, Jinkun Cao, Rawal Khirodkar +4
We present SimXR, a method for controlling a simulated avatar from information (headset pose and cameras) obtained from AR / VR headsets. Due to the challenging viewpoint of head-m…
Multi-Object Tracking by Hierarchical Visual Representations
Jinkun Cao, Jiangmiao Pang, Kris Kitani
We propose a new visual hierarchical representation paradigm for multi-object tracking. It is more effective to discriminate between objects by attending to objects' compositional…
MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction
Jiahe Chen, Jinkun Cao, Dahua Lin +2
To predict future trajectories, the normalizing flow with a standard Gaussian prior suffers from weak diversity. The ineffectiveness comes from the conflict between the fact of asy…
Universal Humanoid Motion Representations for Physics-Based Control
Zhengyi Luo, Jinkun Cao, Josh Merel +4
We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. Due to the high dimensionality of humanoids…