LipGAN
Advanced speech-to-lip synchronization for high-fidelity face-to-face translation.
A high-performance, modular PyTorch toolbox for state-of-the-art face recognition and analysis.
FaceX-Zoo is a specialized open-source framework developed by JD AI Research (JDAI-CV) designed to standardize and accelerate the research and deployment of deep face recognition models. Built on PyTorch, it features a highly modular architecture that decouples backbones, training heads, and evaluation protocols. This allows developers to seamlessly swap components such as ArcFace, CosFace, or AdaFace with various neural architectures like ResNet, MobileFaceNet, or EfficientNet. In the 2026 market, FaceX-Zoo remains a critical asset for organizations seeking to build sovereign AI solutions that prioritize data privacy and edge computing over reliance on proprietary, cloud-based APIs. The toolkit includes built-in support for face detection, alignment, and spoof detection, providing a comprehensive pipeline for end-to-end facial analysis. Its rigorous evaluation scripts provide benchmarking against industry-standard datasets like LFW, CFP-FP, and AgeDB-30, ensuring high-fidelity performance. As facial recognition technology faces increasing regulatory scrutiny, FaceX-Zoo’s transparent codebase allows for the implementation of explainable AI (XAI) and bias mitigation strategies at the architectural level, making it a preferred choice for enterprise-grade, compliant biometric systems.
Architectural separation of feature extractors (backbones) and supervision signals (heads) using a unified interface.
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.
Includes ArcFace, CosFace, SphereFace, Am-Softmax, and AdaFace out-of-the-box.
Native support for multi-GPU training via PyTorch DDP to handle massive datasets.
Built-in scripts for calculating FAR, FRR, and accuracy across LFW, CFP-FP, and CPLFW benchmarks.
Access to a repository of models pre-trained on high-quality datasets like MS1M-V3.
Integrated 5-point landmark detection and face cropping based on MTCNN or RetinaFace.
Utilities to convert PyTorch .pth files to ONNX and MNN formats.
Securely matching a live selfie against a government-issued ID image with high precision.
Registry Updated:2/7/2026
Grant access if similarity exceeds the 1e-6 FAR threshold.
High-throughput facial identification for large groups entering a facility.
Identifying recurring customers to provide personalized shopping experiences without storing PII images.