Towards Consistent Video Geometry Estimation
Summary
ViGeo is a transformer-based foundation model that recovers dense and consistent 3D geometry from videos using dynamic chunking attention and a completion-based data refinement framework, achieving state-of-the-art performance across multiple tasks.
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Paper page - Towards Consistent Video Geometry Estimation
Source: https://huggingface.co/papers/2605.30060 Published on May 28
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Submitted byhttps://huggingface.co/pkqbajng
zhuon May 29
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Abstract
ViGeo is a transformer-based foundation model that recovers dense and consistent 3D geometry from videos using dynamic chunking attention and a completion-based data refinement framework.
This work presents ViGeo, a feed-forward foundation model for recovering spatially dense and temporally consistent geometry from video sequences. Built upon a plaintransformer architecturewithout task-specific architectural modifications, ViGeo supports streaming, full-sequence, and long-video inference within a unified model. The key design isdynamic chunking attention, which exposes the model to both bidirectional and causal temporal contexts during training and allows it to adapt its attention pattern at test time without retraining. To improve supervision quality, we further introduce a completion-based data refinement framework. This framework trains avideo depth completionteacher that conditions on sparse and noisy annotations and exploits video/multi-view contextto produce dense, temporally coherent, and geometrically reliable training targets. Beyond depth and point maps, ViGeo also predictssurface normalswithin the same framework. Trained solely on public datasets, ViGeo achieves state-of-the-art performance across online, offline, and long-videodepth estimation, surface normal estimation, andvideo point map estimation.
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