arXiv (All)
open access
HairFlow: Modeling 3D Hair Strands with Multi-Modal Conditioned Diffusion
Modeling 3D hair strands is challenging due to complex hairstyle geometry, scarce high-quality training data, and incomplete or noisy input observations. Foundation models offer broader hairstyle priors, but their outputs are not directly usable for strand-based rendering and simulation. Existing ap
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
- Neuroll: Real-Time Neural Strand-Based Hair Simulation via Simulator-in-the-Loop Unrolling#1200340
- DualDiff3D: Dual Structure-Appearance Diffusion Priors for Reliability-Enhanced 3D Gaussian Splatting#821549
- 3D Segmentation of Pathological Muscle with a Physics-Informed Latent-Regularized U-Net#1361638
- SPID: Distilled Protein Backbone Generation#1049683
- Leveraging Glycan-Glycan Interactions to Tune the Conformation of Glycan Hairpins and Build Rigid 3D Architectures.#165707
Record · ID 1342224
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