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Latent Bridge: From Video Latents to 3D Geometry via Feature Alignment

Shuai Zhang, Zhanpeng Ouyang, Zhengqing Chen, Ruohong Mei, Shaoyu Chen, Weiqiang Ren, Hui Sun, Qian Zhang, Wenyu Liu, Xinggang Wang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28521 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

视频生成和反馈3D重建通常使用单独的表示,需要RGB解码和视觉重新编码来恢复从生成的隐形物的几何。我们提出了拉丁桥,该桥将视频生成的隐形空间与地理特征空间连接起来,以直接预测视频隐形物的3D地理学,而视频编码器和地理学模型仍然被冻结。我们为Wan2.1和V-RAE开发示范特定的桥梁,允许摄像机,深度和3D点云的反馈预测,而没有中间的RGB路径。每个桥梁都通过特征蒸发来训练RGB视频,而视频编码器和地理学模型仍然被冻结。培训不需要地面真理地理学,也不需要下游地理学集

Machine translation (Meta M2M100); the English original follows.

Abstract

Video generation and feed-forward 3D reconstruction typically use separate representations, requiring RGB decoding and visual re-encoding to recover geometry from generated latents. We propose Latent Bridge, which connects video generative latent spaces with geometric feature spaces to directly predict 3D geometry from video latents. We develop representation-specific bridges for Wan2.1 and V-RAE, enabling feed-forward prediction of cameras, depth, and 3D point clouds without the intermediate RGB pathway. Each bridge is trained on RGB videos through feature distillation, while the video encoder and geometry model remain frozen. Training requires neither ground-truth geometry nor execution of the downstream geometric aggregator or prediction heads. At inference, the same video latents support both RGB decoding and geometry prediction. The same trained bridge can be reused for image-to-video and text-to-video generation within the same video latent space, without bridge retraining. Experiments on Co3Dv2 and WildRGB-D demonstrate competitive geometric reconstruction quality, efficient end-to-end generation and reconstruction, and faster latent-to-geometry decoding, highlighting latent alignment as a practical and reusable interface between video generation and 3D reconstruction. Our code is available at: https://github.com/hustvl/Latent-Bridge.

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Topics: Computer science, Artificial intelligence, Feature (linguistics), RGB color model, Computer vision, Encoder

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