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VLN agents can navigate more effectively by predicting their future states and proactively planning based on forecasted semantic map cues, rather than relying solely on historical context.
MC3D models can now generalize to unseen camera configurations thanks to a new framework that explicitly accounts for spatial prior discrepancies.
Stop training your M3OD models on the same old entangled data: this method decomposes and recomposes objects, scenes, and camera poses to generate diverse training examples on the fly, boosting performance without needing more real-world data.
