Human Pose, Shape and Action
3D Pose from Images
2D Pose from Images
Beyond Motion Capture
Action and Behavior
Body Perception
Body Applications
Pose and Motion Priors
Clothing Models (2011-2015)
Reflectance Filtering
Learning on Manifolds
Markerless Animal Motion Capture
Multi-Camera Capture
2D Pose from Optical Flow
Body Perception
Neural Prosthetics and Decoding
Part-based Body Models
Intrinsic Depth
Lie Bodies
Layers, Time and Segmentation
Understanding Action Recognition (JHMDB)
Intrinsic Video
Intrinsic Images
Action Recognition with Tracking
Neural Control of Grasping
Flowing Puppets
Faces
Deformable Structures
Model-based Anthropometry
Modeling 3D Human Breathing
Optical flow in the LGN
FlowCap
Smooth Loops from Unconstrained Video
PCA Flow
Efficient and Scalable Inference
Motion Blur in Layers
Facade Segmentation
Smooth Metric Learning
Robust PCA
3D Recognition
Object Detection
WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion
] accurately estimates human motion from video using two novel strategies: (1) feature integration from 2D keypoints and pixels and (2) contact-aware trajectory recovery that places the human in a global coordinate system without foot sliding.The estimation of 3D human motion from video has progressed rapidly but current methods still have several key limitations. First, most methods estimate the human in camera coordinates. Second, prior work on estimating humans in global coordinates often assumes a flat ground plane and produces foot sliding. Third, the most accurate methods rely on computationally expensive optimization pipelines, limiting their use to offline applications. Finally, existing video-based methods are surprisingly less accurate than single-frame methods. We address these limitations with WHAM (World-grounded Humans with Accurate Motion), which accurately and efficiently reconstructs 3D human motion in a global coordinate system from video. WHAM learns to lift 2D keypoint sequences to 3D using motion capture data and fuses this with video features, integrating motion context and visual information. WHAM exploits camera angular velocity estimated from a SLAM method together with human motion to estimate the body's global trajectory. We combine this with a contact-aware trajectory refinement method that lets WHAM capture human motion in diverse conditions, such as climbing stairs. WHAM outperforms all existing 3D human motion recovery methods across multiple in-the-wild benchmarks. Code will be available for research purposes at https://wham.is.tue.mpg.de/.
WHAM takes the sequence of 2D keypoints estimated by a pretrained detector and encodes it to the motion feature. WHAM then updates the motion feature using another sequence of image features extracted from the image encoder through the feature integrator. From the updated motion feature, the Local Motion Decoder estimates 3D motion in the camera coordinate system and foot-ground contact probability. The Trajectory Decoder takes the motion feature and camera angular velocity to initially estimate the global root orientation and egocentric velocity, which are then updated through the Trajectory Refiner using the foot-ground contact. The final output of WHAM is pixel-aligned 3D human motion with the 3D trajectory in the global coordinates.
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