arXiv (All)
open access
MoCAR: Motion-code Coordinate-aware AutoRegression for Continuous Trajectory Forecasting
Autoregressive generation is natural for language, where predicted tokens can be directly reused as the next prediction state, but trajectory forecasting lacks such a clean token: motion is continuous, multimodal, and expressed in local coordinate frames that evolve with the predicted trajectory. We
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
- Parameter-Efficient Temporal Domain Generalization#1354124
- ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models#444599
- Repurposing Pre-trained LLMs as High Fidelity Continuous Text Autoencoders#1063459
- Calibrate the Decisions That Change the Future: On-Policy Post-Training Quantization for Multimodal Large Language Models#1132029
- MoVT: Video-Augmented Motion Tokenizer for Text-to-Motion Generation#1084869
Record · ID 1202629
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