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Silvia Zuffi

10 accepted papers

2025

Generative Zoo

ICCV 2025poster

The model-based estimation of 3D animal pose and shape from images enables computational modeling of animal behavior. Training models for this purpose requires large amounts of labeled image data with precise pose and shape annotations. However, capturing such data requires the use of multi-view or…

Cited by 0SourcePDFScholar
2024

VAREN: Very Accurate and Realistic Equine Network

CVPR 2024poster

Data-driven three-dimensional parametric shape models of the human body have gained enormous popularity both for the analysis of visual data and for the generation of synthetic humans. Following a similar approach for animals does not scale to the multitude of existing animal species not to mention…

Cited by 9SourcePDFScholar
2023

BITE: Beyond Priors for Improved Three-D Dog Pose Estimation

CVPR 2023poster

We address the problem of inferring the 3D shape and pose of dogs from images. Given the lack of 3D training data, this problem is challenging, and the best methods lag behind those designed to estimate human shape and pose. To make progress, we attack the problem from multiple sides at once. First,…

Cited by 29SourcePDFScholar
2022

BARC: Learning To Regress 3D Dog Shape From Images by Exploiting Breed Information

CVPR 2022poster

Our goal is to recover the 3D shape and pose of dogs from a single image. This is a challenging task because dogs exhibit a wide range of shapes and appearances, and are highly articulated. Recent work has proposed to directly regress the SMAL animal model, with additional limb scale parameters, fro…

Cited by 52PDFScholar
2019

Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture From Images "In the Wild"

ICCV 2019poster

We present the first method to perform automatic 3D pose, shape and texture capture of animals from images acquired in-the-wild. In particular, we focus on the problem of capturing 3D information about Grevy's zebras from a collection of images. The Grevy's zebra is one of the most endangered specie…

Cited by 187PDFcodeScholar
2018

Lions and Tigers and Bears: Capturing Non-Rigid, 3D, Articulated Shape From Images

CVPR 2018poster

Animals are widespread in nature and the analysis of their shape and motion is important in many fields and industries. Modeling 3D animal shape, however, is difficult because the 3D scanning methods used to capture human shape are not applicable to wild animals or natural settings. Consequently, we…

Cited by 154SourcePDFScholar
2017

3D Menagerie: Modeling the 3D Shape and Pose of Animals

CVPR 2017spotlight

There has been significant work on learning realistic, articulated, 3D models of the human body. In contrast, there are few such models of animals, despite many applications. The main challenge is that animals are much less cooperative than humans. The best human body models are learned from thousan…

Cited by 491PDFScholar