LoGo: Local-Global Rewards for Consistent Long-Horizon Video Generation

Ziqi Ma; Shreya Sharma; Mohamed El Banani; Katja Schwarz; Chongjie Ye; Chao-Yuan Wu; Li Fei-Fei; Ben Mildenhall; Georgia Gkioxari; Justin Johnson; Gowthami Somepalli

arXiv · 2610.03636

LoGo: Local-Global Rewards for Consistent Long-Horizon Video Generation

Ziqi Ma·Shreya Sharma·Mohamed El Banani·Katja Schwarz·Chongjie Ye·Chao-Yuan Wu·Li Fei-Fei·Ben Mildenhall·Georgia Gkioxari·Justin Johnson·Gowthami Somepalli

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Abstract

Camera-controlled video models are rapidly advancing toward long generation horizons and complex camera control. A key failure mode is 3D inconsistency: as the camera moves, objects lose permanence and scene structures shift. Existing post-training techniques, which assign a single scalar reward to the entire generation, are poorly suited to correcting these inconsistencies over long horizons. We introduce LoGo, which blends global and spatially localized rewards for camera-controlled video models. The local reward provides fine-grained credit assignment, which substantially improves 3D consistency, while the global reward preserves camera following and video quality. Across three base models, LoGo shows a clear advantage on DL3DV and TrajectoryBench, a new benchmark for long-horizon, complex-camera-control generation that current evaluations lack. LoGo effectively reduces local object shifts, artifacts, and global scene changes, illustrating the importance of credit assignment in post-training video models. Project website: https://ziqi-ma.github.io/logo-website/

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Ziqi Ma, Shreya Sharma, Mohamed El Banani, Katja Schwarz, Chongjie Ye, Chao-Yuan Wu, Li Fei-Fei, Ben Mildenhall, Georgia Gkioxari, Justin Johnson, Gowthami Somepalli. 2026-10-02. LoGo: Local-Global Rewards for Consistent Long-Horizon Video Generation. https://arxiv.org/abs/2610.03636

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