Another feature video for Lovart — this time Move Object, an AI capability for
repositioning objects within an image. The feature first, then how we made the
video.
What the Feature Does
Move Object lets you quickly adjust where a single element sits in a photo: draw
a box around the target, the system identifies the main object; confirm, drag to
a new spot, and get an updated image. After the move, the original area fills in
automatically so the frame stays natural and complete.
Three Core Capabilities
Select & Auto-Identify
Enter Move Object and box the area you want to change. The system reads the
primary object from your selection — not simple masking, but semantic
understanding of foreground versus background.
Move to Any Position
Once identified, drag the object anywhere in the frame. Shift a person from
center to the right for title space, nudge a product for better balance,
reposition supporting elements — seconds, not hours.
Auto-Fill the Original Area
After the object moves, the system fills the gap from the original image content
and outputs a complete new frame. This is generative editing: the subject stays
intact, old and new positions connect naturally — not pixel shuffling.
That's the product side. The production challenge: this is an operation chain,
not a single feature bullet.
Traditional Three Steps vs. AI One Step
Repositioning an object in an image used to mean: lasso or pen to cut it out,
content-aware fill or clone stamp for the hole, then lighting and blending so it
"belongs" in the new spot. Three steps minimum, each a failure point.
Move Object reframes that as identify → rewrite position → generate — one
user-facing flow: select → point → see the result.
The Narrative Problem of Operation Chains
Viewers have seconds to grasp three things at once: what the AI is doing
(identification), how it's changing things (position rewrite), and what you get
(the new image). Skip one link, and Move Object stops making sense.
Let the Chain Speak for Itself
Same approach as before — let the operation chain tell the story. Identification
shots stress the moment of "understanding" when the selection lands: the AI
knows foreground from background. Position rewrite uses one continuous drag to
spell out spatial change: from where, to where, what happened in between. The
result hold gives time to absorb that the original spot was filled in.
Pacing shouldn't be even: identification fast (awareness), rewrite steady
(trust), result with pause (control).
Overall production & compositing by @Leo Wang
From "How to Use It" to "What It Did for You"
When AI compresses a complex edit chain into one step, visual narrative shifts
from teaching the tool to showing what the AI did on your behalf — translating
invisible computation into spatial relationships you can feel.