arXiv (All)
open access
DirtyMoCap: Robust Motion Capture from Unconstrained Markers
Optical motion capture delivers high-fidelity human motion, but its reliance on strict marker layouts and clean trajectories severely limits its real-world applicability. In practice, tracking systems frequently output unconstrained markers: sparse, noisy, and unordered point clouds with unknown or
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- TAPVid-MV: A Benchmark for Tracking Any Point in 3D Across Multiple Views#822318
- Tetris: Tile-level Sampling for Efficient and High-Fidelity Video Object Tracking#652321
- Tetris: Tile-level Sampling for Efficient and High-Fidelity Video Object Tracking#650868
- Tetris: Tile-level Sampling for Efficient and High-Fidelity Video Object Tracking#229610
- Dual Covariance Gaussian Splatting SLAM: Decoupling Rendering and Registration for Robust Real-Time Tracking#1061379
Record · ID 1014972
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive