arXiv (All)
open access
MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose
Reconstructing multi-human body meshes from a single monocular image is a challenging problem, requiring both individual body meshes and relative 3D positions to produce a coherent scene representation. We introduce MUG (Multi-hUman Graph network), which uses a single graph neural network to constru
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
- DSR-Net: Learning Dynamic Skeletal Relations for 3D Human Pose Estimation#1325395
- Reconstructing Humans and Objects in Interaction using Large Reconstruction Models#1215456
- ULIFLOW: Unified Latent Human-Object Interaction Flow#1388852
- STA-TFM: Spatio-Temporal Aggregation Across Views TransForMer for Pose Estimation#1050656
- Skeletons in Flow: Graph Structured Flow Matching for Human Motion Prediction#1116562
Record · ID 1490300
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