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Found 11 papers, 2 papers with code
The Stitched Puppet: A Graphical Model of 3D Human Shape and Pose
We propose a new 3D model of the human body that is both realistic and part-based.
3D Menagerie: Modeling the 3D shape and pose of animals
The best human body models are learned from thousands of 3D scans of people in specific poses, which is infeasible with live animals.
Lions and Tigers and Bears: Capturing Non-Rigid, 3D, Articulated Shape From Images
Animals are widespread in nature and the analysis of their shape and motion is important in many fields and industries.
Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"
In contrast to research on human pose, shape and texture estimation, training data for endangered species is limited, the animals are in complex natural scenes with occlusion, they are naturally camouflaged, travel in herds, and look similar to each other.
hSMAL: Detailed Horse Shape and Pose Reconstruction for Motion Pattern Recognition
In this paper we present our preliminary work on model-based behavioral analysis of horse motion.
Seeing biodiversity: perspectives in machine learning for wildlife conservation
Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices.
Fish sounds: towards the evaluation of marine acoustic biodiversity through data-driven audio source separation
Moreover, one of the causes of biodiversity loss is sound pollution; in data obtained from regions with loud anthropic noise, it is hard to separate the artificial from the fish sound manually.
BARC: Learning to Regress 3D Dog Shape from Images by Exploiting Breed Information
But, even with a better shape model, the problem of regressing dog shape from an image is still challenging because we lack paired images with 3D ground truth.
OSSO: Obtaining Skeletal Shape from Outside
We address the problem of inferring the anatomic skeleton of a person, in an arbitrary pose, from the 3D surface of the body; i. e. we predict the inside (bones) from the outside (skin).
BITE: Beyond Priors for Improved Three-D Dog Pose Estimation
To that end, we exploit contact with the ground as a form of side information.
AWOL: Analysis WithOut synthesis using Language
This involves learning a mapping between the latent space of a vision-language model and the parameter space of the 3D model, which we do using a small set of shape and text pairs.
