LipGAN
Advanced speech-to-lip synchronization for high-fidelity face-to-face translation.
Advanced 3D object reconstruction from single-view 2D images using Graph Convolutional Networks.
Mesh R-CNN is a cutting-edge deep learning framework developed by Meta AI Research (FAIR) that extends the capabilities of Mask R-CNN to the 3D domain. In the 2026 landscape of spatial computing, Mesh R-CNN remains a foundational architecture for systems requiring precise volumetric understanding from monocular camera inputs. The architecture operates in two primary stages: first, it utilizes a standard 2D backbone to perform object detection and instance segmentation; second, it employs a novel voxel-to-mesh head that predicts a coarse volumetric representation and refines it into a high-fidelity triangle mesh using Graph Convolutional Networks (GCNs). This hybrid approach overcomes the limitations of traditional voxel-based methods (which suffer from cubic complexity) and point-cloud methods (which lack surface topology). By integrating seamlessly with PyTorch3D, Mesh R-CNN enables end-to-end differentiable rendering and training, making it highly effective for AR/VR asset creation, autonomous navigation, and digital twin synthesis. Its ability to handle complex occlusions and diverse object categories while producing manifold surfaces positions it as a critical utility for developers bridging the gap between 2D perception and 3D action.
A specialized neural head that predicts a 3D voxel grid and converts it into a mesh via a differentiable Vert-Align operator.
Advanced speech-to-lip synchronization for high-fidelity face-to-face translation.
The semantic glue between product attributes and consumer search intent for enterprise retail.
The industry-standard multimodal transformer for layout-aware document intelligence and automated information extraction.
Photorealistic 4k upscaling via iterative latent space reconstruction.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses GCNs to iteratively adjust vertex positions to match the object's fine-grained surface details.
Jointly optimizes for 2D bounding boxes, segmentation masks, voxel occupancy, and mesh chamfer distance.
Leverages the highly optimized Detectron2 framework for feature extraction and ROI pooling.
Specifically designed to output meshes that maintain water-tight properties when possible.
Supports gradients flowing from the rendered 2D image back to the 3D mesh vertices.
Calculates the distance between predicted and ground truth surface points to minimize geometric error.
Manually creating 3D models for product catalogs is expensive and slow.
Registry Updated:2/7/2026
Display in AR viewer
Robots need to understand the 3D volume of obstacles, not just 2D boxes.
Predicting how a 2D furniture photo will fit into a 3D physical room.