Papers › Fusing Posture and Position Representations for Point Cloud-Based Hand Gesture Recognition

Fusing Posture and Position Representations for Point Cloud-Based Hand Gesture Recognition

6 Jan 20223DV 2022 1archive 2025-07-28

Alexander Bigalke, Mattias P Heinrich

Hand gesture recognition can benefit from directly processing 3D point cloud sequences, which carry rich geometric information and enable the learning of expressive spatio-temporal features. However, currently employed single-stream models cannot sufficiently capture multi-scale features that include both fine-grained local posture variations and global hand movements. We therefore propose a novel dual-stream model, which decouples the learning of local and global features. These are eventually fused in an LSTM for temporal modelling. To induce the global and local stream to capture complementary position and posture features, we propose the use of different 3D learning architectures in both streams. Specifically, state-of-the-art point cloud networks excel at capturing fine posture variations from raw point clouds in the local stream. To track hand movements in the global stream, we combine an encoding with residual basis point sets and a fully-connected DenseNet. We evaluate the method on the Shrec'17 and DHG dataset and report state-of-the-art results at a reduced computational cost. Source code is available at https://github.com/multimodallearning/hand-gesture-posture-position.

Paper PDF Code

Code

multimodallearning/hand-gesture-posture-position mentioned in paperpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Gesture Recognition Hand Gesture Recognition Hand-Gesture Recognition

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Hand Gesture Recognition	DHG-14	FPPR-PCD	Accuracy	92.0	#5 of 13	Archive leaderboard	report
Hand Gesture Recognition	DHG-28	FPPR-PCD	Accuracy	91.7	#4 of 9	Archive leaderboard	report
Hand Gesture Recognition	SHREC 2017	FPPR-PCD	14 Gestures Accuracy	96.1	#2 of 4	Archive leaderboard	report
Hand Gesture Recognition	SHREC 2017	FPPR-PCD	28 Gestures Accuracy	95.2	#2 of 4	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization Concatenated Skip Connection Convolution Dense Block Dense Connections Dropout Global Average Pooling Kaiming Initialization LSTM Max Pooling ReLU Sigmoid Activation Softmax Tanh Activation

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections