arXiv (All)
open access
Reward-Decomposed Reinforcement Learning for Immersive Video Role-Playing
Text-based role-playing models can imitate character styles, but often fail to capture scene atmosphere and evolving tension, which are crucial for immersive applications such as VR games and interactive narratives. We study video-grounded role-playing dialogue and introduce EBM-RL (Eye--Brain--Mout
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
- First Make It Playable, Then Make It Good: Staged Interaction Learning for Small Dialogue-Game Agents#780971
- First Make It Playable, Then Make It Good: Staged Interaction Learning for Small Dialogue-Game Agents#779596
- VrT: A Framework to Model Embodied VR Interaction as a Language-Like Sequence for Emotion Inference#370201
- Electroencephalogram-Based Analysis of Monomodal and Multimodal Interaction in Mixed Reality Games.#317599
- Deep-based semantic representation for avatars in online immersive applications#1090455
Record · ID 1105032
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