Map2Vid: 人間に扱いやすい動き制御を備えた身体性ナビゲーション動画生成
Map2Vid: Embodied Navigation Video Generation with Human-Friendly Motion Control
BEVマップから一人称視点のナビゲーション動画を生成する新タスクを提案し、10万件超のデータセットとベンチマークを構築した研究。
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著者: Yulin Dai, Yuqian Fu, Tianwen Qian, Zhen Xing, Bojia Zi, Xiaoling Wang
分類: cs.CV
原文アブストラクト
Controllable video generation has recently achieved impressive visual quality, yet existing control paradigms remain poorly suited to embodied navigation. Text prompts are spatially ambiguous, while camera-trajectory control relies on low-level motion specifications that are unintuitive for human users and difficult to manipulate. In this work, we introduce Map2Vid, a novel task that reformulates embodied navigation video generation as a BEV-map-to-video synthesis problem, together with a large-scale benchmark for systematic evaluation. By using BEV maps, which provide a visual representation of global scene layout and intended navigation routes, Map2Vid enables human-friendly navigation video generation. To support this task, we construct a dedicated dataset with over 100K navigation sequences across 61 indoor scenes, each paired with egocentric videos, BEV maps, camera trajectories and language instructions. We further develop a unified framework that integrates multiple control signals, together with a curriculum training strategy that progressively weakens non-map inputs, enabling flexible inference under both full conditioning and map-only input. Extensive experiments across multiple foundation generation models demonstrate that controllable navigation video generation via BEV map is a viable and promising direction.
