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Found 17 papers, 10 papers with code
HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Video
Since humans interact with diverse objects every day, the holistic 3D capture of these interactions is important to understand and model human behaviour.
HUGS: Human Gaussian Splats
We achieve state-of-the-art rendering quality with a rendering speed of 60 FPS while being ~100x faster to train over previous work.
PACE: Human and Camera Motion Estimation from in-the-wild Videos
This design combines the strengths of SLAM and motion priors, which leads to significant improvements in human and camera motion estimation.
Physically Plausible Full-Body Hand-Object Interaction Synthesis
Through a hierarchical framework, we first learn skill priors for both body and hand movements in a decoupled setting.
Human-Object Interaction Detection
Reinforcement Learning (RL)
Reconstructing Action-Conditioned Human-Object Interactions Using Commonsense Knowledge Priors
We further qualitatively evaluate the effectiveness of our method on real images and demonstrate its generalizability towards interaction types and object categories.
TempCLR: Reconstructing Hands via Time-Coherent Contrastive Learning
We introduce TempCLR, a new time-coherent contrastive learning approach for the structured regression task of 3D hand reconstruction.
ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation
In part this is because there exist no datasets with ground-truth 3D annotations for the study of physically consistent and synchronised motion of hands and articulated objects.
Human-Aware Object Placement for Visual Environment Reconstruction
In fact, we demonstrate that these human-scene interactions (HSIs) can be leveraged to improve the 3D reconstruction of a scene from a monocular RGB video.
D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions
We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose.
Learning to Regress Bodies from Images using Differentiable Semantic Rendering
For Minimally-Clothed regions, we define the DSR-MC loss, which encourages a tight match between a rendered SMPL body and the minimally-clothed regions of the image.
Ranked #43 on
3D Human Pose Estimation
on 3DPW
(using extra training data)
SPEC: Seeing People in the Wild with an Estimated Camera
We then train a novel network that concatenates the camera calibration to the image features and uses these together to regress 3D body shape and pose.
Ranked #1 on
3D Multi-Person Pose Estimation
on AGORA
Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-pixel Part Segmentation
In natural conversation and interaction, our hands often overlap or are in contact with each other.
Ranked #5 on
3D Interacting Hand Pose Estimation
on InterHand2.6M
PARE: Part Attention Regressor for 3D Human Body Estimation
Despite significant progress, we show that state of the art 3D human pose and shape estimation methods remain sensitive to partial occlusion and can produce dramatically wrong predictions although much of the body is observable.
Ranked #2 on
3D Multi-Person Pose Estimation
on AGORA
3D human pose and shape estimation
3D Multi-Person Pose Estimation
Analytical Moment Regularizer for Training Robust Networks
In this work, we propose a new training regularizer that aims to minimize the probabilistic expected training loss of a DNN subject to a generic Gaussian input.
VIBE: Video Inference for Human Body Pose and Shape Estimation
Human motion is fundamental to understanding behavior.
Ranked #32 on
Monocular 3D Human Pose Estimation
on Human3.6M
Self-Supervised Learning of 3D Human Pose using Multi-view Geometry
Training accurate 3D human pose estimators requires large amount of 3D ground-truth data which is costly to collect.
Ranked #1 on
Weakly-supervised 3D Human Pose Estimation
on Human3.6M
(Number of Frames Per View metric)
Self-Supervised Learning
Weakly-supervised 3D Human Pose Estimation
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network
In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose estimation architecture that combines a multi-task model with a novel assignment method.
Ranked #8 on
Multi-Person Pose Estimation
on COCO
