This technical overview details the engineering requirements for implementing a browser-local video face-swapping pipeline using WebGPU, WebCodecs, and ONNX Runtime Web. To ensure usability, the architecture avoids processing full 1080p frames through the generator, instead using a multi-stage resolution strategy: SCRFD detection at 640×640, identity extraction at 112×112, and face generation at 224×224. The pipeline utilizes Lucas–Kanade optical flow for landmark propagation between anchor frames, employing a forward-backward consistency check to reject unstable tracks. To optimize performance, the system downloads models in parallel but initializes WebGPU sessions serially to prevent GPU memory spikes and shader compilation jitter. Memory management is handled by transferring ArrayBuffer ownership via Web Workers to avoid expensive structured cloning of large tensors (e.g., a 640×640 Float32 RGB tensor totaling ~4.69 MB). Post-generation, the pipeline applies morphological operations (dilation and erosion) and bounded color matching using mean and standard deviation scaling to ensure seamless blending. Finally, the implementation emphasizes strict resource lifecycle management, requiring explicit disposal of ONNX tensors, OpenCV matrices, and ImageBitmaps to prevent memory leaks caused by the JavaScript garbage collector's inability to immediately reclaim non-JS heap memory.