ELMO: Enhanced Real-time LiDAR Motion Capture through Upsampling

Byeoli Choi; Deok-Kyeong Jang; Deok-Yun Jang; Donghoon Shin; Dongseok Yang; Sung-Hee Lee

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ELMO: Enhanced Real-time LiDAR Motion Capture through Upsampling

Deok-Kyeong Jang, Dongseok Yang, Deok-Yun Jang, Byeoli Choi, Donghoon Shin, Sung-hee Lee

Graphics · Submitted 9 Oct 2024

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The authors' main claim ELMO's fast inference time makes it well-suited for real-time applications, exemplified in our demo video featuring live streaming and interactive gaming scenarios.

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arXiv ID2410.06963 (version 2)

Pith IDLDHO7MPC, Pith's permanent record of this paper and its review

CitationByeoli Choi, Deok-Kyeong Jang, Deok-Yun Jang, Donghoon Shin, Dongseok Yang, Sung-Hee Lee. "ELMO: Enhanced Real-time LiDAR Motion Capture through Upsampling." arXiv:2410.06963 (2024). https://pith.science/paper/LDHO7MPC

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What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.08 · human participants 0.94 · controlled comparison 0.77 · randomized assignment 0.01 · states quantitative result 0.98 · compares with prior results 0.98 · mentions code or data availability 0.99.

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This paper introduces ELMO, a real-time upsampling motion capture framework designed for a single LiDAR sensor. Modeled as a conditional autoregressive transformer-based upsampling motion generator, ELMO achieves 60 fps motion capture from a 20 fps LiDAR point cloud sequence. The key feature of ELMO is the coupling of the self-attention mechanism with thoughtfully designed embedding modules for motion and point clouds, significantly elevating the motion quality. To facilitate accurate motion capture, we develop a one-time skeleton calibration model capable of predicting user skeleton offsets from a single-frame point cloud. Additionally, we introduce a novel data augmentation technique utilizing a LiDAR simulator, which enhances global root tracking to improve environmental understanding. To demonstrate the effectiveness of our method, we compare ELMO with state-of-the-art methods in both image-based and point cloud-based motion capture. We further conduct an ablation study to validate our design principles. ELMO's fast inference time makes it well-suited for real-time applications, exemplified in our demo video featuring live streaming and interactive gaming scenarios. Furthermore, we contribute a high-quality LiDAR-mocap synchronized dataset comprising 20 different subjects performing a range of motions, which can serve as a valuable resource for future research. The dataset and evaluation code are available at {\blue \url{https://movin3d.github.io/ELMO_SIGASIA2024/}}

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What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	This paper introduces ELMO, a real-time upsampling motion capture framework designed for a single LiDAR sensor.	contribution 1.00	0.82	0.10	asserted 1.00
s1	Modeled as a conditional autoregressive transformer-based upsampling motion generator, ELMO achieves 60 fps motion capture from a 20 fps LiDAR point cloud sequence.	method 0.43	0.94	0.23	asserted 1.00
s2	The key feature of ELMO is the coupling of the self-attention mechanism with thoughtfully designed embedding modules for motion and point clouds, significantly elevating the motion quality.	method 0.80	0.75	0.13	asserted 1.00
s3	To facilitate accurate motion capture, we develop a one-time skeleton calibration model capable of predicting user skeleton offsets from a single-frame point cloud.	contribution 0.52	0.97	0.03	asserted 1.00
s4	Additionally, we introduce a novel data augmentation technique utilizing a LiDAR simulator, which enhances global root tracking to improve environmental understanding.	contribution 0.68	0.96	0.03	asserted 1.00
s5	To demonstrate the effectiveness of our method, we compare ELMO with state-of-the-art methods in both image-based and point cloud-based motion capture.	method 0.67	0.93	0.06	asserted 0.98
s6	We further conduct an ablation study to validate our design principles.	method 0.70	0.96	0.04	asserted 0.94
s7	ELMO's fast inference time makes it well-suited for real-time applications, exemplified in our demo video featuring live streaming and interactive gaming scenarios.	interpretation 0.70	0.87	0.16	asserted 0.97
s8	Furthermore, we contribute a high-quality LiDAR-mocap synchronized dataset comprising 20 different subjects performing a range of motions, which can serve as a valuable resource for future research.	contribution 1.00	0.96	0.07	asserted 0.99
s9	The dataset and evaluation code are available at {\blue \url{https://movin3d.github.io/ELMO_SIGASIA2024/}}	contribution 0.90	0.64	0.03	asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.97.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

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Original abstract

This paper introduces ELMO, a real-time upsampling motion capture framework designed for a single LiDAR sensor. Modeled as a conditional autoregressive transformer-based upsampling motion generator, ELMO achieves 60 fps motion capture from a 20 fps LiDAR point cloud sequence. The key feature of ELMO is the coupling of the self-attention mechanism with thoughtfully designed embedding modules for motion and point clouds, significantly elevating the motion quality. To facilitate accurate motion capture, we develop a one-time skeleton calibration model capable of predicting user skeleton offsets from a single-frame point cloud. Additionally, we introduce a novel data augmentation technique utilizing a LiDAR simulator, which enhances global root tracking to improve environmental understanding. To demonstrate the effectiveness of our method, we compare ELMO with state-of-the-art methods in both image-based and point cloud-based motion capture. We further conduct an ablation study to validate our design principles. ELMO's fast inference time makes it well-suited for real-time applications, exemplified in our demo video featuring live streaming and interactive gaming scenarios. Furthermore, we contribute a high-quality LiDAR-mocap synchronized dataset comprising 20 different subjects performing a range of motions, which can serve as a valuable resource for future research. The dataset and evaluation code are available at {\blue \url{https://movin3d.github.io/ELMO_SIGASIA2024/}}

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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Uses the method or tool of 3 papers

We then utilize Mini-PointNet [ref] to project these sub-body-patch clouds into point embeddings.

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation · Motion and Point Cloud Embedding · 0.99
For the joint feature embedding, we use spatial-temporal graph convolutional blocks (STGCN) [ref] to maintain the local joint graph information of the human skeleton as much as possible.

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition · Motion and Point Cloud Embedding · 1.00
Inspired by PointBERT [ref] , we group each point cloud into several body part patches for each frame $i$.

Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling · Motion and Point Cloud Embedding · 0.90

Compares with 1 paper

We quantitatively compare our results with SOTA image-based pose tracking methods including VIBE [ref] , MotionBERT [ref] , and NIKI [ref] .

MotionBERT: A Unified Perspective on Learning Human Motion Representations · Quantitative Evaluation · 1.00

Cites as background 7 papers

Prior data-driven methods continuously predict body shape and motion together from input sequences [ref] .

LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment · Skeleton Calibration Model · 0.98
Prior data-driven methods continuously predict body shape and motion together from input sequences [ref] .

EgoPoser: Robust Real-Time Egocentric Pose Estimation from Sparse and Intermittent Observations Everywhere · Skeleton Calibration Model · 0.98
The latest image-based human pose tracking methods [ref] have shown impressive quality through a two-step process involving 2D keypoint extraction and SMPL body model [ref] fitting.

Humans in 4D: Reconstructing and Tracking Humans with Transformers · LiDAR-based Pose Tracking · 0.86

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