A framework generates consistent multi-view scenes from one freehand sketch via a ~9k-sample dataset, Parallel Camera-Aware Attention Adapters, and Sparse Correspondence Supervision Loss, outperforming baselines in realism and consistency.
In: European Conference on Computer Vision
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GEM-4D is a video world model that injects 4D correspondence supervision to improve geometric consistency and robot manipulation success from 61% to 81%.
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Geometrically Consistent Multi-View Scene Generation from Freehand Sketches
A framework generates consistent multi-view scenes from one freehand sketch via a ~9k-sample dataset, Parallel Camera-Aware Attention Adapters, and Sparse Correspondence Supervision Loss, outperforming baselines in realism and consistency.
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GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation
GEM-4D is a video world model that injects 4D correspondence supervision to improve geometric consistency and robot manipulation success from 61% to 81%.