Realistic Deepfake Video Generation
Budget: $8 – $15 USD
I need an AI specialist to architect and implement a pipeline that generates highly realistic videos with a focus on deepfake creation. The goal is to produce short-form clips where faces are swapped or re-animated so seamlessly that the edits are imperceptible to the viewer.
Scope of the first milestone
• Design or select a state-of-the-art face-swap / reenactment model (e.g., StyleGAN, Diffusion-based, or a custom GAN).
• Prepare the data pipeline: automated face alignment, anonymisation, and dataset versioning.
• Train, fine-tune, and iterate until we reach photorealistic quality with minimal artefacts.
• Deliver an inference script that takes a source video plus target images and outputs the final deepfake clip.
• Provide a short written walkthrough covering hardware requirements, model parameters, and tips for further tuning.
Acceptance criteria
1. Frame-by-frame identity preservation ≥ 95 % (verified with face-recognition scores).
2. No temporal flicker visible on 30-fps playback.
3. End-to-end generation time under 2× video length on a single high-end GPU.
Tech stack keywords: PyTorch, TensorFlow, FFmpeg, CUDA, Google Colab, facial-landmark detection, GAN inversion.
Roadmap beyond this delivery
Once the core system is proven, I plan to expand into other AI-driven video features—scene synthesis, automated dubbing, even real-time object tracking—so clean, well-documented code is essential for future extension.
Ready to start as soon as we agree on the approach, and open to your suggestions on model selection or workflow improvements.
Scope of the first milestone
• Design or select a state-of-the-art face-swap / reenactment model (e.g., StyleGAN, Diffusion-based, or a custom GAN).
• Prepare the data pipeline: automated face alignment, anonymisation, and dataset versioning.
• Train, fine-tune, and iterate until we reach photorealistic quality with minimal artefacts.
• Deliver an inference script that takes a source video plus target images and outputs the final deepfake clip.
• Provide a short written walkthrough covering hardware requirements, model parameters, and tips for further tuning.
Acceptance criteria
1. Frame-by-frame identity preservation ≥ 95 % (verified with face-recognition scores).
2. No temporal flicker visible on 30-fps playback.
3. End-to-end generation time under 2× video length on a single high-end GPU.
Tech stack keywords: PyTorch, TensorFlow, FFmpeg, CUDA, Google Colab, facial-landmark detection, GAN inversion.
Roadmap beyond this delivery
Once the core system is proven, I plan to expand into other AI-driven video features—scene synthesis, automated dubbing, even real-time object tracking—so clean, well-documented code is essential for future extension.
Ready to start as soon as we agree on the approach, and open to your suggestions on model selection or workflow improvements.