Vox-adv-cpk.pth.tar Link [UPDATED]

Place the file in the project root or a checkpoints/ folder.

While several repositories use this checkpoint, the most famous is (by Rudrabha Mukhopadhyay et al., IIIT Hyderabad). Wav2Lip revolutionized the space by achieving "lip-sync that is so good, it's scary." The Vox-adv-cpk.pth.tar file is typically the pre-trained generator or discriminator from the Wav2Lip ecosystem.

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If you need help this file (e.g., loading it in PyTorch, converting it, or checking its contents safely), let me know and I can provide specific code.

One of the most common questions from newcomers is the distinction between vox-cpk.pth.tar and vox-adv-cpk.pth.tar . While both are pre-trained on the VoxCeleb dataset, they represent different training approaches: Vox-adv-cpk.pth.tar

This dual objective forces the generator to produce sharper, more realistic outputs with finer details and fewer artifacts.

: The model framework uses the pre-trained weights to automatically detect coordinate points on the human face without needing manual markers. Place the file in the project root or a checkpoints/ folder

Vox-adv-cpk.pth.tar expects strict input dimensions. The standard model was trained on 256x256 pixel resolution . If your source image or driving video frames are not pre-resized to exactly 256x256, the model will throw a matrix shape error.

: This component analyzes a frame from the driving video and identifies a sparse set of "keypoints" (typically 10 in the VoxCeleb model). These are not facial landmarks like eyes and nose, but rather learnable, abstract points that represent the structure and motion of the face. The model uses these keypoints to understand the movement of the driving subject. : If you need help this file (e