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Benchmarking Monocular 3D Dog Pose Estimation Using In-The-Wild Motion Capture Data
To address this, we created 3DDogs-Wild, a naturalised version of the dataset where the optical markers are in-painted and the subjects are placed in diverse environments, enhancing its utility for training RGB image-based pose detectors.
SyDog: A Synthetic Dog Dataset for Improved 2D Pose Estimation
Estimating the pose of animals can facilitate the understanding of animal motion which is fundamental in disciplines such as biomechanics, neuroscience, ethology, robotics and the entertainment industry.
Ranked #1 on
Animal Pose Estimation
on StanfordExtra
Total capture: 3D human pose estimation fusing video and inertial sensors
We incorporate this model within a dual stream network integrating pose embeddings derived from MVV and a forward kinematic solve of the IMU data.
Ranked #11 on
3D Human Pose Estimation
on Total Capture
FaceDirector: Continuous Control of Facial Performance in Video
We present a method to continuously blend between multiple facial performances of an actor, which can contain different facial expressions or emotional states.
