“UMO Stills” — Multi‑Identity Consistency with an OmniGen2‑Class Image Base (Pattern Overview)

TL;DR “UMO stills” refers to a production pattern for multi‑identity consistency: curate identity‑clean stills with a strong image base (e.g., an OmniGen2‑class generator), index them in an identity bank, then use retrieval‑guided conditioning (plus optional LoRA adapters) to keep subjects consistent across long or multi‑scene videos.

What problem does it solve?

Long or multi‑scene videos with several recurring subjects (actors, hosts, avatars) often drift in face details, outfits, or accessories. Typical video models can maintain a single identity in a short clip, but multi‑identity and long‑form projects need stronger anchors and repeatable retrieval.

“UMO stills” addresses this by:

• Creating identity‑clean, high‑SNR still frames for each subject
• Indexing those stills with robust embeddings for retrieval
• Feeding anchor imagery and retrieval hints back into the video pipeline

This elevates identity stability while keeping creative control (pose, camera motion, scene swaps) intact.

Core components

1. OmniGen2-class image base (stills)
   • Use a modern text-image generator (OmniGen2-class) to produce or refine identity-clean stills (front/¾ profile, neutral expression, key outfits).
   • Enforce quality gates: resolution ≥ 1024 px, sharpness, exposure, and minimal motion blur; crop to consistent head/torso framing.
2. Identity bank (embeddings + metadata)
   • Compute embeddings with a face/ID model and a general visual encoder (CLIP-family). Store per-identity vectors plus rich tags (hair, outfit, glasses, accessories).
   • Deduplicate with cosine thresholding; maintain a curated “gold” set.
3. Retrieval-guided conditioning (video)
   • At generation time, query the bank by prompt + rough frame description to fetch the nearest anchor still(s).
   • Condition the video model with anchor crops (concat channels, reference frames, or adapter inputs) and prompt constraints.
   • Optionally blend LoRA adapters per identity/outfit for stronger lock-in.
4. Consistency checks and feedback
   • During generation, run face/ID similarity on sampled frames. If drift > τ, nudge guidance (increase identity weight, swap anchor, or re-seed).
   • For long-takes, insert “refresh” keyframes (UMO stills) at scene boundaries.

Suggested pipeline (high level)

1. Curate stills
   • Generate/refine 6–12 stills per identity with the image base (clean backgrounds, neutral to expressive variants).