MoCap-solver

Chen, Kang; Wang, Yupan; Zhang, Song-Hai; Xu, Sen‐Zhe; Zhang, Weidong; Hu, Shi‐Min

doi:10.1145/3476576.3476641

Cited by 3 publications

(10 citation statements)

References 20 publications

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“…Recently, with the development of deep learning, some researchers [1][2][3][26][27][28] attempt to train nonlinear neural networks to learn the feature space of motions in a data-driven way. Holden 1 proposed a simple FC network equipped with residual connections to directly produce the global transformations of human joints from input raw markers frame-by-frame.…”

Section: Related Workmentioning

confidence: 99%

“…However, such a per-frame way can not fully consider and leverage the temporal information of MoCap data, tending to break temporal continuity of motion sequences. On the contrary, Chen et al 3 presented a two-stage framework, named MoCap-Solver, to obtain human motions from a sequence of input raw markers. To the best of our knowledge, MoCap-Solver 3 is the state-of-the-art solution to the specific task of motion solving from marker-based raw MoCap data until of the time of our research.…”

Section: Related Workmentioning

confidence: 99%

“…Let X ∈ R T×M×3 and Y ∈ R T×M×3 denote a temporal sequence of 3D positions of raw markers and clean markers respectively, where T is the number of frames in the temporal sequence and M is the number of markers in each frame. As pointed by MoCap-Solver, 3 the 3D global positions of markers depend on three kinds of intrinsic information: skeleton template, skeleton motion, and marker configuration (see Figure 2a). Specifically, the skeleton template Y t ∈ R J×3 corresponds to the human body shape and it is defined by 3D local offset of each joint relative to its parent in T-pose, where J is the number of joints.…”

Section: Preliminariesmentioning

confidence: 99%

“…For a fair comparison, we follow the same marker order in previous work, 3 which conforms to the original data format exported by MoCap systems, as the default order to implement our model. Given the divided marker subsets, we further employ a series of local operators to capture local spatio-temporal relationships among different marker subsets.…”

Section: Divide-and-conquer Strategymentioning

confidence: 99%

“…However, they do not fully consider and leverage the temporal information of MoCap data, which tends to break temporal continuity of motion sequences. To tackle the above issue, Chen et al 3 present a state-of-the-art framework, named MoCap-Solver, to predict human skeleton motion sequences from the whole sequential raw markers using FC layers. Since they only consider and utilize the global correlation among all markers within the whole sequence but do not effectively model the spatio-temporal information of marker subsets within local ranges, their method might generate sub-optimal results with inaccurate local details when complicated human motions occur (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

MarkerNet: A divide‐and‐conquer solution to motion capture solving from raw markers

Hu,

Tang,

et al. 2024

Computer Animation & Virtual

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Marker‐based optical motion capture (MoCap) aims to localize 3D human motions from a sequence of input raw markers. It is widely used to produce physical movements for virtual characters in various games such as the role‐playing game, the fighting game, and the action‐adventure game. However, the conventional MoCap cleaning and solving process is extremely labor‐intensive, time‐consuming, and usually the most costly part of game animation production. Thus, there is a high demand for automated algorithms to replace costly manual operations and achieve accurate MoCap cleaning and solving in the game industry. In this article, we design a divide‐and‐conquer‐based MoCap solving network, dubbed MarkerNet, to estimate human skeleton motions from sequential raw markers effectively. In a nutshell, our key idea is to decompose the task of direct solving of global motion from all markers into first modeling sub‐motions of local parts from the corresponding marker subsets and then aggregating sub‐motions into a global one. In this manner, our model can effectively capture local motion patterns w.r.t. different marker subsets, thus producing more accurate results compared to the existing methods. Extensive experiments on both real and synthetic data verify the effectiveness of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Section: Divide-and-conquer Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MarkerNet: A divide‐and‐conquer solution to motion capture solving from raw markers

Hu,

Tang,

et al. 2024

Computer Animation & Virtual

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RoMo: A Robust Solver for Full-body Unlabeled Optical Motion Capture

Pan,

Zheng,

Jiang

et al. 2024

SIGGRAPH Asia 2024 Conference Papers

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and adjusts the template skeleton to match estimated joint positions. We demonstrate that RoMo achieves high labeling and solving accuracy across multiple metrics and various datasets. Extensive comparisons show that our method outperforms state-of-the-art research methods. On a real dataset, RoMo improves the F1 score of hand labeling from 0.94 to 0.98, and reduces joint position error of body motion solving by 25%. Furthermore, RoMo can be applied in scenarios where commercial systems are inadequate. The code and data for RoMo are available at https:// github.com/ non-void/ RoMo.

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MoCapDT: Temporal Guided Motion Capture Solving with Diffusion Transfer

2023

jeis

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We present an approach to reconstruct the joint location from noisy marker position in 4D data. The 4D data means the 3D location of different markers and time sequence. At the core of our approach, we apply a modified diffusion model architecture to transfer and denoise the raw marker information in the latent space under the guidance of other temporal data. Then we decode the latent space to not real skeleton 3D space. This enable us not only utilize the temporal guidance, we further utilize the iterative denoising technique to exploit the potential in the diffusion network. Furthermore, we demonstrate that our work outperform auto-encoder based deep learning model by large margin during our experiment on CMU-Synthesized data set and some real-world dataset provided by NC-SOFT.

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MoCap-solver

Cited by 3 publications

References 20 publications

MarkerNet: A divide‐and‐conquer solution to motion capture solving from raw markers

MarkerNet: A divide‐and‐conquer solution to motion capture solving from raw markers

RoMo: A Robust Solver for Full-body Unlabeled Optical Motion Capture

MoCapDT: Temporal Guided Motion Capture Solving with Diffusion Transfer

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