# ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal References

Source: [ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal References](<https://www.sota2.com/research/paper/68fa699f1e209131cea232a0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-24T15:39:46.772Z

Authors: Yannan He, Anqi Pang, Xin Chen, Han Liang, Minye Wu, Yuexin Ma, Lan Xu

Published: 2021-03-11T15:49:22.000Z

## Abstract

Capturing challenging human motions is critical for numerous applications, but it suffers from complex motion patterns and severe self-occlusion under the monocular setting. In this paper, we propose ChallenCap -- a template-based approach to capture challenging 3D human motions using a single RGB camera in a novel learning-and-optimization framework, with the aid of multi-modal references. We propose a hybrid motion inference stage with a generation network, which utilizes a temporal encoder-decoder to extract the motion details from the pair-wise sparse-view reference, as well as a motion discriminator to utilize the unpaired marker-based references to extract specific challenging motion characteristics in a data-driven manner. We further adopt a robust motion optimization stage to increase the tracking accuracy, by jointly utilizing the learned motion details from the supervised multi-modal references as well as the reliable motion hints from the input image reference. Extensive experiments on our new challenging motion dataset demonstrate the effectiveness and robustness of our approach to capture challenging human motions.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [3D Human Motion Capture on ChallenCap Dataset](<https://www.sota2.com/research/sota/3d-human-motion-capture-on-challencap-dataset.md>): 3D Human Motion Capture; ChallenCap Dataset; MPJPE (mm): 52.6.

- [3D Human Motion Capture on HCM (test)](<https://www.sota2.com/research/sota/3d-human-motion-capture-on-hcm-test.md>): 3D Human Motion Capture; HCM (test); MPJPE: 69.5.

- [3D Human Motion Capture on AIST++ (test)](<https://www.sota2.com/research/sota/3d-human-motion-capture-on-aist-plus-plus-test.md>): 3D Human Motion Capture; AIST++ (test); MPJPE: 53.2.

- [3D Human Pose Estimation on 3DPW (test)](<https://www.sota2.com/research/sota/3d-human-pose-estimation-on-3dpw-test.md>): 3D Human Pose Estimation; 3DPW (test); PA-MPJPE: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2103.06747>)

[PDF](<https://arxiv.org/pdf/2103.06747.pdf>)