| Stage | Tools & Techniques | Typical Time (per 30‑sec clip) | |-------|-------------------|--------------------------------| | | Scraping public videos, facial landmarks extraction (MediaPipe), audio harvesting. | 2–4 h | | Model Fine‑Tuning | Transfer‑learning on Mondo‑Forge, few‑shot adaptation using LoRA (Low‑Rank Adaptation). | 6–12 h (GPU‑accelerated) | | Scene Composition | Blender for 3‑D background insertion; Photoshop for matte painting. | 1–2 h | | Rendering | NVIDIA RTX 4090 clusters, mixed‑precision FP16 inference. | 30 min | | Post‑Processing | Color grading, artifact removal (Topaz Video AI), watermark embedding. | 1 h |
How can we better the use of digital likenesses in fan-driven communities? Unit21https://www.unit21.ai mondomonger deepfakes
The intersection of fan culture and generative artificial intelligence has birthed a complex new landscape. At the center of this evolution is , a term frequently associated with the distribution and creation of high-fidelity "deepfakes"—synthetic media where a person's likeness is replaced with another using deep learning. | Stage | Tools & Techniques | Typical