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Joachim Tesch

8 accepted papers

2026

MAMMA: Markerless Accurate Multi-person Motion Acquisition

CVPR 2026

We present MAMMA, a markerless motion-capture pipeline that accurately recovers SMPL-X parameters from multi-view video. Traditional motion-capture systems rely on physical markers. Although they offer high accuracy, their requirements of specialized hardware, manual marker placement, and extensive

Cited by 0SourcecodeScholar
2025

BEDLAM2.0: Synthetic humans and cameras in motion

NeurIPS 2025oral

Inferring 3D human motion from video remains a challenging problem with many applications. While traditional methods estimate the human in image coordinates, many applications require human motion to be estimated in world coordinates. This is particularly challenging when there is both human and cam…

Cited by 0SourceScholar
2023

BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion

CVPR 2023highlight

We show, for the first time, that neural networks trained only on synthetic data achieve state-of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real images. Previous synthetic datasets have been small, unrealistic, or lacked realistic clothing. Achieving sufficient…

Cited by 158SourcePDFScholar
2022

Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation

ECCV 2022poster

"Virtual facial avatars will play an increasingly important role in immersive communication, games and the metaverse, and it is therefore critical that they be inclusive. This requires accurate recovery of the albedo, regardless of age, sex, or ethnicity. While significant progress has been made on…

Cited by 35SourcePDFScholar
2021

AGORA: Avatars in Geography Optimized for Regression Analysis

CVPR 2021poster

While the accuracy of 3D human pose estimation from images has steadily improved on benchmark datasets, the best methods still fail in many real-world scenarios. This suggests that there is a domain gap between current datasets and common scenes containing people. To obtain ground-truth 3D pose, cur…

Cited by 245PDFcodeScholar
2021

Populating 3D Scenes by Learning Human-Scene Interaction

CVPR 2021poster

Humans live within a 3D space and constantly interact with it to perform tasks. Such interactions involve physical contact between surfaces that is semantically meaningful. Our goal is to learn how humans interact with scenes and leverage this to enable virtual characters to do the same. To that end…

Cited by 167PDFcodeScholar
2021

SPEC: Seeing People in the Wild With an Estimated Camera

ICCV 2021poster

Due to the lack of camera parameter information for in-the-wild images, existing 3D human pose and shape (HPS) estimation methods make several simplifying assumptions: weak-perspective projection, large constant focal length, and zero camera rotation. These assumptions often do not hold and we show,…

Cited by 160PDFcodeScholar
2016

The CableRobot simulator large scale motion platform based on cable robot technology

IROS 2016poster

This paper introduces the CableRobot simulator, which was developed at the Max Planck Institute for Biological Cybernetics in cooperation with the Fraunhofer Institute for Manufacturing Engineering and Automation IPA. The simulator is a completely novel approach to the design of motion simulation pl…

Cited by 178SourceScholar