Kedis ku xidhan hagaha · Adag Heerka
Sawirka Sawirka Kediska Sawirka Keliya
Waxay tijaabisaa wareejinta dhaqdhaqaaqa dhibcaha ku salaysan, maaraynta xidhidhka, SadTalker iyo LivePortrait nashqadeynta xulashada weji hal sawir.
Su'aal 1 ee 8
How does the First Order Motion Model obtain its keypoints?
Horudhac su'aal kasta8 su'aalo
Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.
- How does the First Order Motion Model obtain its keypoints?
- From manually labeled facial landmarks
- From a 3D scan of the person
- It learns them without labels during training
- From the audio track
- What is the purpose of the occlusion map?
- To mark regions the source image cannot supply, so they must be painted in
- To blur the background on purpose
- To detect the speaker's language
- To choose the output frame rate
- What is the benefit of relative motion transfer?
- It doubles the frame rate
- The source keeps its own face shape instead of taking on the driver's
- It removes the need for a driving video
- It makes the head turn farther
- What does SadTalker predict from the audio?
- Raw pixel values of the mouth
- A new background
- Qoraal qoraal ah
- 3D Morphable Model coefficients for expression and head pose
- What does LivePortrait's stitching module do?
- It pastes the animated face back into the full image without misaligned shoulders
- It merges two different people's faces
- It stitches audio clips together
- It converts video to GIF
- Why do large head turns usually fail in single-photo animation?
- The GPU runs out of memory
- Head turns are disabled by default
- The photo has no information about unseen regions like the side of the head
- Audio cannot describe rotation
- Why do keypoint methods warp feature maps rather than raw pixels?
- Pixels cannot be moved
- It lets the decoder repair stretching and fill unseen regions
- Feature maps are always higher resolution
- It removes the need for keypoints
- In implicit 3D keypoint methods, what does the driving frame contribute?
- The canonical keypoints encoding identity
- The background texture
- The skin color
- Head rotation, translation and expression deformations