How do you evaluate AI-generated clothing video clips before approving them?

Evaluate every AI-generated clothing clip against five fabric-specific checks: weave and texture accuracy, color stability across lighting, garment edge integrity (seams, hems, silhouette), drape and motion realism on the body, and environmental interaction (wind, touch, sunlight). If two or more checks fail, reject and regenerate — don't lock it.

Run the clip through these five checks in order, the same way you'd review on-set takes:

1. Weave and texture accuracy. Pause on three frames — opening, mid, closing — and confirm the weave pattern matches your reference fabric close-up. Watch for texture flicker between frames (surface shimmering or shifting unnaturally), which is the most common AI tell on knits and linens. If the material looks plastic or generically smooth, reject — that's the failure mode the Standing Don'ts in your treatment exist to catch.

2. Color stability across lighting. Scrub the clip and check the garment color holds as the character moves through light changes. Backlit, side-lit, and shadow frames should read as the same fabric, not a different dye lot. White or cream garments against pale backgrounds are a known consistency failure point — flag those harder.

3. Garment edge integrity. Check seams, hems, collars, and silhouette frame-by-frame at cut points. Edges that morph, hems that re-stitch themselves between beats, or a silhouette that subtly changes shape are immediate rejects — they break product consistency more visibly than any texture issue.

4. Drape and motion realism on the body. The fabric must move with the body, not float, stick, or freeze. Heavier fabrics (denim, wool) should resist motion; lighter fabrics (linen, silk) should respond to it. If the clip was generated with an over-literal stillness instruction, you'll see the model freeze stiff — one micro-gesture in the prompt fixes it, but the clip itself is a reject.

5. Environmental interaction. Confirm the fabric responds to whatever it's interacting with in the shot — wind catching a hem, sunlight raking across a weave, a hand pressing into the cloth. This is where the per-shot fabric direction note in your shooting script pays off; if the interaction reads wrong, the direction note was either missing or ignored, and a regeneration with a sharper note is faster than salvaging.

Decision rule. One failed check, regenerate that shot with a targeted note (e.g. "linen falls heavier on the shoulder, hem catches the wind on the second beat"). Two or more failed checks, the underlying reference or context is off — fix that before generating again, not the clip. As a benchmark across documented productions, roughly 85% of generated clips get rejected on the way to a final cut (one production used 12 of 25 clips, another used 10 of 39, another used 1 of 11) — high rejection is normal and the reason image-first iteration matters: lock the still frame against these same five checks before spending video credits.

The invideo agent supports this review natively — clicking any generation reveals the exact prompt and attachments used, so a rejection comes with the diagnostic information you need to fix the next pass. As Hridaye, invideo's creative director, puts it: "What I did have to iterate on was the framing. The fabric and its movement and interactions with the environment were largely on point all throughout all of these generations." Frame the five checks accordingly — framing fixes are cheap reshoots; fabric-behavior fails usually mean the texture language or reference set needs work upstream.

Watch some of these to see what works for you:

What I did have to iterate on was the framing. The fabric and its movement and interactions with the environment were largely on point all throughout all of these generations.

— Hridaye, invideo's creative director