34 citations · 57 across the 17 of their papers we have counts for
18 papers
Scalable Multi-Human Cooperative Manipulation via Commander-Executor Coordination
Buzhen Huang, Xingchen Wu, Yuanwang Yang +2
Multi-human cooperative object manipulation is a highly challenging topic in humanoid control. While existing methods can handle simple collaborative carrying tasks, they struggle…
Feed-Forward Multi-view Multi-person Reconstruction with Contrastive Human-Aware 3D Representation
Yuanwang Yang, Buzhen Huang, Zongxuan Ren +2
Multi-view human reconstruction has been extensively studied under simplified settings, yet robust and efficient multi-person reconstruction in unconstrained environments remains c…
Stability-Driven Motion Generation for Object-Guided Human-Human Co-Manipulation
Jiahao Xu, Xiaohan Yuan, Xingchen Wu +3
Co-manipulation requires multiple humans to synchronize their motions with a shared object while ensuring reasonable interactions, maintaining natural poses, and preserving stable…
Contrastive Multi-Modal Hypergraph Reasoning for 3D Crowd Mesh Recovery
Minghao Sun, Chongyang Xu, Yitao Xie +2
Multi-person 3D reconstruction is pivotal for real-world interaction analysis, yet remains challenging due to severe occlusions and depth ambiguity. Current approaches typically re…
E-React: Towards Emotionally Controlled Synthesis of Human Reactions
Chen Zhu, Buzhen Huang, Zijing Wu +2
Emotion serves as an essential component in daily human interactions. Existing human motion generation frameworks do not consider the impact of emotions, which reduces naturalness…
Reconstructing Close Human Interaction with Appearance and Proxemics Reasoning
Buzhen Huang, Chen Li, Chongyang Xu +4
Due to visual ambiguities and inter-person occlusions, existing human pose estimation methods cannot recover plausible close interactions from in-the-wild videos. Even state-of-the…
TokenHSI: Unified Synthesis of Physical Human-Scene Interactions through Task Tokenization
Liang Pan, Zeshi Yang, Zhiyang Dou +5
Synthesizing diverse and physically plausible Human-Scene Interactions (HSI) is pivotal for both computer animation and embodied AI. Despite encouraging progress, current methods m…
Adapting Human Mesh Recovery with Vision-Language Feedback
Chongyang Xu, Buzhen Huang, Chengfang Zhang +2
Human mesh recovery can be approached using either regression-based or optimization-based methods. Regression models achieve high pose accuracy but struggle with model-to-image ali…
Simultaneously Recovering Multi-Person Meshes and Multi-View Cameras with Human Semantics
Buzhen Huang, Jingyi Ju, Yuan Shu +1
Dynamic multi-person mesh recovery has broad applications in sports broadcasting, virtual reality, and video games. However, current multi-view frameworks rely on a time-consuming…
Generalizable Human Gaussians from Single-View Image
Jinnan Chen, Chen Li, Jianfeng Zhang +4
In this work, we tackle the task of learning 3D human Gaussians from a single image, focusing on recovering detailed appearance and geometry including unobserved regions. We introd…
Closely Interactive Human Reconstruction with Proxemics and Physics-Guided Adaption
Buzhen Huang, Chen Li, Chongyang Xu +3
Existing multi-person human reconstruction approaches mainly focus on recovering accurate poses or avoiding penetration, but overlook the modeling of close interactions. In this wo…
CrowdRec: 3D Crowd Reconstruction from Single Color Images
Buzhen Huang, Jingyi Ju, Yangang Wang
This is a technical report for the GigaCrowd challenge. Reconstructing 3D crowds from monocular images is a challenging problem due to mutual occlusions, server depth ambiguity, an…
Reconstructing Groups of People with Hypergraph Relational Reasoning
Buzhen Huang, Jingyi Ju, Zhihao Li +1
Due to the mutual occlusion, severe scale variation, and complex spatial distribution, the current multi-person mesh recovery methods cannot produce accurate absolute body poses an…
Physics-Guided Human Motion Capture with Pose Probability Modeling
Jingyi Ju, Buzhen Huang, Chen Zhu +2
Incorporating physics in human motion capture to avoid artifacts like floating, foot sliding, and ground penetration is a promising direction. Existing solutions always adopt kinem…
Synthesizing Physically Plausible Human Motions in 3D Scenes
Liang Pan, Jingbo Wang, Buzhen Huang +4
We present a physics-based character control framework for synthesizing human-scene interactions. Recent advances adopt physics simulation to mitigate artifacts produced by data-dr…
Occluded Human Body Capture with Self-Supervised Spatial-Temporal Motion Prior
Buzhen Huang, Yuan Shu, Jingyi Ju +1
Although significant progress has been achieved on monocular maker-less human motion capture in recent years, it is still hard for state-of-the-art methods to obtain satisfactory r…
Neural MoCon: Neural Motion Control for Physically Plausible Human Motion Capture
Buzhen Huang, Liang Pan, Yuan Yang +2
Due to the visual ambiguity, purely kinematic formulations on monocular human motion capture are often physically incorrect, biomechanically implausible, and can not reconstruct ac…
Dynamic Multi-Person Mesh Recovery From Uncalibrated Multi-View Cameras
Buzhen Huang, Yuan Shu, Tianshu Zhang +1
Dynamic multi-person mesh recovery has been a hot topic in 3D vision recently. However, few works focus on the multi-person motion capture from uncalibrated cameras, which mainly f…