A Novel Framework for Automated Warehouse Layout Generation

Optimizing warehouse layouts is crucial due to its significant impact on efficiency and productivity. We present an AI-driven framework for automated warehouse layout generation. This framework employs constrained beam search to derive optimal layouts within given spatial parameters, adhering to all functional requirements. The feasibility of the generated layouts is verified based on criteria such as item accessibility, required minimum clearances, and aisle connectivity. A scoring function is then used to evaluate the feasible layouts considering the number of storage locations, access points, and accessibility costs. We demonstrate our method's ability to produce feasible, optimal layouts for a variety of warehouse dimensions and shapes, diverse door placements, and interconnections. This approach, currently being prepared for deployment, will enable human designers to rapidly explore and confirm options, facilitating the selection of the most appropriate layout for their use-case.

MaskRenderer: 3D-Infused Multi-Mask Realistic Face Reenactment

We present a novel end-to-end identity-agnostic face reenactment system, MaskRenderer, that can generate realistic, high fidelity frames in real-time. Although recent face reenactment works have shown promising results, there are still significant challenges such as identity leakage and imitating mouth movements, especially for large pose changes and occluded faces. MaskRenderer tackles these problems by using (i) a 3DMM to model 3D face structure to better handle pose changes, occlusion, and mouth movements compared to 2D representations; (ii) a triplet loss function to embed the cross-reenactment during training for better identity preservation; and (iii) multi-scale occlusion, improving inpainting and restoring missing areas. Comprehensive quantitative and qualitative experiments conducted on the VoxCeleb1 test set, demonstrate that MaskRenderer outperforms state-of-the-art models on unseen faces, especially when the Source and Driving identities are very different.