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arXiv Computer Vision By Shuai Zhang, Zhanpeng Ouyang, Zhengqing Chen, Ruohong Mei, Shaoyu Chen, Weiqiang Ren, Hui Sun, Qian Zhang, Wenyu Liu, Xinggang Wang

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

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Latent Bridge connects video generative latent spaces with geometric feature spaces, enabling direct prediction of 3D geometry—cameras, depth, and point clouds—without an intermediate RGB decoding step. The method trains representation‑specific bridges for Wan2.1 and V‑RAE via feature distillation on RGB videos while keeping the video encoder and geometry model frozen, requiring no ground‑truth geometry. At inference, the same video latents support both RGB decoding and geometry prediction, and the trained bridge can be reused across image‑to‑video and text‑to‑video generation, achieving competitive reconstruction quality on Co3Dv2 and WildRGB‑D with efficient end‑to‑end generation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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