Search Results for author:
Found 8 papers, 2 papers with code
HOI-M3:Capture Multiple Humans and Objects Interaction within Contextual Environment
Humans naturally interact with both others and the surrounding multiple objects, engaging in various social activities.
BOTH2Hands: Inferring 3D Hands from Both Text Prompts and Body Dynamics
We further provide a strong baseline method, BOTH2Hands, for the novel task: generating vivid two-hand motions from both implicit body dynamics and explicit text prompts.
I'M HOI: Inertia-aware Monocular Capture of 3D Human-Object Interactions
In this paper, we present I'm-HOI, a monocular scheme to faithfully capture the 3D motions of both the human and object in a novel setting: using a minimal amount of RGB camera and object-mounted Inertial Measurement Unit (IMU).
IKOL: Inverse kinematics optimization layer for 3D human pose and shape estimation via Gauss-Newton differentiation
This paper presents an inverse kinematic optimization layer (IKOL) for 3D human pose and shape estimation that leverages the strength of both optimization- and regression-based methods within an end-to-end framework.
Ranked #24 on
3D Human Pose Estimation
on 3DPW
NeuralDome: A Neural Modeling Pipeline on Multi-View Human-Object Interactions
We construct a dense multi-view dome to acquire a complex human object interaction dataset, named HODome, that consists of $\sim$75M frames on 10 subjects interacting with 23 objects.
Weakly Supervised 3D Multi-person Pose Estimation for Large-scale Scenes based on Monocular Camera and Single LiDAR
Motivated by this, we propose a monocular camera and single LiDAR-based method for 3D multi-person pose estimation in large-scale scenes, which is easy to deploy and insensitive to light.
Mutual Adaptive Reasoning for Monocular 3D Multi-Person Pose Estimation
This method first uses 2. 5D pose and geometry information to infer camera-centric root depths in a forward pass, and then exploits the root depths to further improve representation learning of 2. 5D pose estimation in a backward pass.
Compressed Sensing for Photoacoustic Computed Tomography Using an Untrained Neural Network
Our method can cooperate with other existing regularization, and further improve the quality.
