Wav2Lip is a deep learning model developed by researchers at IIIT Hyderabad that generates perfectly synchronized lip movements from any audio recording, representing a breakthrough in visual speech synthesis. The model takes a face video and an audio track as input, then produces realistic lip movements that precisely match the spoken content while preserving the original facial identity, expressions, and head movements. Built on a GAN (Generative Adversarial Network) architecture, Wav2Lip employs a pre-trained lip-sync discriminator that ensures the generated mouth movements are perceptually indistinguishable from real speech. This discriminator evaluates sync quality at a fine-grained level, resulting in significantly more accurate lip synchronization than previous approaches. The model works with any face regardless of identity, ethnicity, or language, and handles various audio types including speech, singing, and dubbed content. Wav2Lip operates on pre-recorded videos as well as static images which it animates with speech-driven lip movements. Released under the Apache 2.0 license, it is fully open source and has been widely adopted by the content creation community. Common applications include dubbing foreign language films, creating multilingual video content, animating avatars and virtual characters, producing educational materials with synthetic presenters, and accessibility applications for hearing-impaired users. The model can process videos at reasonable speeds on consumer GPUs and integrates with popular video editing pipelines for professional production workflows.