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We just tested what might be the most accurate motion transfer workflow we've seen so far. It's a little more involved than a standard workflow, but the results are pretty impressive. Here's the basic workflow: 1. Start with your reference footage. Create character and environment references. 2. Convert the... show more
30,770 次观看 • 1 个月前 •via X (Twitter)
9 条评论

Why not just upload the original video as a reference instead of processing it first

So shouldn't the generative services be creating a depth map at the same time as the generated video? Might be something they should offer.

The problem with the skeleton workflow (in addition to the occasional glitchiness) was that it was overlayed on top of the depth map footage and then being fed to the AI, which is why you were getting clones/duplicates in the output.

Excellent video. Detailed. Thorough. And I like that you didn't show everything magically working. The failures are more informative than the successes. Thanks for this!

Depth maps making motion transfer this clean is genuinely impressive work 🎥

Using depth maps as the motion cue feels right, I noticed far fewer artifacts than pure skeleton feeds. The extra conversion step adds overhead but the consistency is worth it. Rule: let depth drive the transfer

the reason depth-first beats skeleton-only tracking: depth maps carry occlusion and body volume, not just joint positions, so the generator has real spatial info instead of guessing what's behind an arm or how a character fills out a frame. stacking depth + segmentation + pose is multi-signal conditioning — removing ambiguity before generation even starts, not just throwing more tools at it.

The depth pass preserves something skeleton guidance keeps losing here: the actor’s weight through turns and occlusions. I’d test the face separately—the body can be accurate while the intention of the performance disappears.

The annoying part is that the workflow is still pretty involved, but the result makes it look worth it. Motion transfer is getting scary good.
