Faces of people who do not exist: a style-based GAN, truncation, latent-space edits, selfie inversion, provenance and memorisation audits.
Engineers designing AI products and the systems that serve them.
Your approach: Explain the AI behavior and system boundaries in the brief, with evidence for the requested quality, privacy, latency and cost.
Design Realistic Face Generation. Faces of people who do not exist: a style-based GAN, truncation, latent-space edits, selfie inversion, provenance and memorisation audits. Work from the scoping questions below. State assumptions for any unspecified load, guarantee or target, then trace your design end to end. Explain one difficult case and a credible alternative; the worked example is a reference, not a required implementation.
Resolve the scoping questions for Realistic Face Generation. Separate stated behavior from assumptions, and identify what is outside your design.
Declare relevant volume, latency, freshness, quality or cost targets with units. Show calculations or an evaluation plan that can test them; unspecified targets are your assumptions, not hidden pass criteria.
Explain how your guarantees hold in a difficult case relevant to this subject. Address: Synthetic-data orders? Video or animation?
Identify users, required behavior and exclusions. Answer: Who uses it? Our own model?
Define the information owned by the system and the inputs, outputs and errors at its boundaries. Resolve: Which controls? Real photos?
Estimate the dominant workload and resource demand with units and explicit assumptions. For a learned system, also state how quality is measured and what data is available.
Draw or describe the responsibilities needed for Realistic Face Generation. Trace a representative request, event or job from its input to a visible result; identify durable state owners.
Walk through a difficult case step by step, including detection and recovery. Consider: How fast? Provenance? Synthetic-data orders? Video or animation?
Compare a credible alternative using your chosen workload and guarantees. Explain a remaining risk, a signal to watch and when you would change the design.