Now you can replace the head.
Example: https://www.youtube.com/watch?v=xr5FHd0AdlQ
Requirements:
Post processing skill in Adobe After Effects or Davinci Resolve.
Usage:
1) Find suitable dst footage with the monotonous background behind head
2) Use “extract head” script
3) Gather rich src headset from only one scene (same color and haircut)
4) Mask whole head for src and dst using XSeg editor
5) Train XSeg
6) Apply trained XSeg mask for src and dst headsets
7) Train SAEHD using ‘head’ face_type as regular deepfake model with DF archi. You can use pretrained model for head. Minimum recommended resolution for head is 224.
8) Extract multiple tracks, using Merger:
a. Raw-rgb
b. XSeg-prd mask
c. XSeg-dst mask
9) Using AAE or DavinciResolve, do:
a. Hide source head using XSeg-prd mask: content-aware-fill, clone-stamp, background retraction, or other technique
b. Overlay new head using XSeg-dst mask
Warning: Head faceset can be used for whole_face or less types of training only with XSeg masking.
XSegEditor: added button ‘view trained XSeg mask’, so you can see which frames should be masked to improve mask quality.
5.XSeg) data_dst/src mask for XSeg trainer - fetch.bat
Copies faces containing XSeg polygons to aligned_xseg\ dir.
Useful only if you want to collect labeled faces and reuse them in other fakes.
Now you can use trained XSeg mask in the SAEHD training process.
It’s mean default ‘full_face’ mask obtained from landmarks will be replaced with the mask obtained from the trained XSeg model.
use
5.XSeg.optional) trained mask for data_dst/data_src - apply.bat
5.XSeg.optional) trained mask for data_dst/data_src - remove.bat
Normally you don’t need it. You can use it, if you want to use ‘face_style’ and ‘bg_style’ with obstructions.
XSeg trainer : now you can choose type of face
XSeg trainer : now you can restart training in “override settings”
Merger: XSeg-* modes now can be used with all types of faces.
Therefore old MaskEditor, FANSEG models, and FAN-x modes have been removed,
because the new XSeg solution is better, simpler and more convenient, which costs only 1 hour of manual masking for regular deepfake.
with XSeg model you can train your own mask segmentator of dst(and src) faces
that will be used in merger for whole_face.
Instead of using a pretrained model (which does not exist),
you control which part of faces should be masked.
Workflow is not easy, but at the moment it is the best solution
for obtaining the best quality of whole_face's deepfakes using minimum effort
without rotoscoping in AfterEffects.
new scripts:
XSeg) data_dst edit.bat
XSeg) data_dst merge.bat
XSeg) data_dst split.bat
XSeg) data_src edit.bat
XSeg) data_src merge.bat
XSeg) data_src split.bat
XSeg) train.bat
Usage:
unpack dst faceset if packed
run XSeg) data_dst split.bat
this scripts extracts (previously saved) .json data from jpg faces to use in label tool.
run XSeg) data_dst edit.bat
new tool 'labelme' is used
use polygon (CTRL-N) to mask the face
name polygon "1" (one symbol) as include polygon
name polygon "0" (one symbol) as exclude polygon
'exclude polygons' will be applied after all 'include polygons'
Hot keys:
ctrl-N create polygon
ctrl-J edit polygon
A/D navigate between frames
ctrl + mousewheel image zoom
mousewheel vertical scroll
alt+mousewheel horizontal scroll
repeat for 10/50/100 faces,
you don't need to mask every frame of dst,
only frames where the face is different significantly,
for example:
closed eyes
changed head direction
changed light
the more various faces you mask, the more quality you will get
Start masking from the upper left area and follow the clockwise direction.
Keep the same logic of masking for all frames, for example:
the same approximated jaw line of the side faces, where the jaw is not visible
the same hair line
Mask the obstructions using polygon with name "0".
run XSeg) data_dst merge.bat
this script merges .json data of polygons into jpg faces,
therefore faceset can be sorted or packed as usual.
run XSeg) train.bat
train the model
Check the faces of 'XSeg dst faces' preview.
if some faces have wrong or glitchy mask, then repeat steps:
split
run edit
find these glitchy faces and mask them
merge
train further or restart training from scratch
Restart training of XSeg model is only possible by deleting all 'model\XSeg_*' files.
If you want to get the mask of the predicted face in merger,
you should repeat the same steps for src faceset.
New mask modes available in merger for whole_face:
XSeg-prd - XSeg mask of predicted face -> faces from src faceset should be labeled
XSeg-dst - XSeg mask of dst face -> faces from dst faceset should be labeled
XSeg-prd*XSeg-dst - the smallest area of both
if workspace\model folder contains trained XSeg model, then merger will use it,
otherwise you will get transparent mask by using XSeg-* modes.
Some screenshots:
label tool: https://i.imgur.com/aY6QGw1.jpg
trainer : https://i.imgur.com/NM1Kn3s.jpg
merger : https://i.imgur.com/glUzFQ8.jpg
example of the fake using 13 segmented dst faces
: https://i.imgur.com/wmvyizU.gifv
Basic usage instruction: https://i.imgur.com/w7LkId2.jpg
'whole_face' requires skill in Adobe After Effects.
For using whole_face you have to extract whole_face's by using
4) data_src extract whole_face
and
5) data_dst extract whole_face
Images will be extracted in 512 resolution, so they can be used for regular full_face's and half_face's.
'whole_face' covers whole area of face include forehead in training square,
but training mask is still 'full_face'
therefore it requires manual final masking and composing in Adobe After Effects.
added option 'masked_training'
This option is available only for 'whole_face' type.
Default is ON.
Masked training clips training area to full_face mask,
thus network will train the faces properly.
When the face is trained enough, disable this option to train all area of the frame.
Merge with 'raw-rgb' mode, then use Adobe After Effects to manually mask, tune color, and compose whole face include forehead.
added option Eyes priority (y/n)
fix eye problems during training ( especially on HD architectures )
by forcing the neural network to train eyes with higher priority
before/after https://i.imgur.com/YQHOuSR.jpg
It does not guarantee the right eye direction.
added smooth_rect option
default is ON.
Decreases jitter of predicting rect by using temporal interpolation.
You can disable this option if you have problems with dynamic scenes.
Removed the wait at first launch for most graphics cards.
Increased speed of training by 10-20%, but you have to retrain all models from scratch.
SAEHD:
added option 'use float16'
Experimental option. Reduces the model size by half.
Increases the speed of training.
Decreases the accuracy of the model.
The model may collapse or not train.
Model may not learn the mask in large resolutions.
true_face_training option is replaced by
"True face power". 0.0000 .. 1.0
Experimental option. Discriminates the result face to be more like the src face. Higher value - stronger discrimination.
Comparison - https://i.imgur.com/czScS9q.png
added FacesetEnhancer
4.2.other) data_src util faceset enhance best GPU.bat
4.2.other) data_src util faceset enhance multi GPU.bat
FacesetEnhancer greatly increases details in your source face set,
same as Gigapixel enhancer, but in fully automatic mode.
In OpenCL build it works on CPU only.
Please consider a donation.
More stable and precise version of the face transformation matrix.
Now full_faces are aligned with the upper and lateral boundaries of the frame,
result: fix of cutted mouth, increase area of the cheeks of side faces
before/after https://i.imgur.com/t9IyGZv.jpg
therefore, additional training is required for existing models.
Optionally, you can re-extract dst faces of your project, if they have problems with cutted mouth or cheeks.
removed TrueFace model.
added SAEv2 model. Differences from SAE:
+ default e_ch_dims is now 21
+ new encoder produces more stable face and less scale jitter
before: https://i.imgur.com/4jUcol8.gifv
after: https://i.imgur.com/lyiax49.gifv - scale of the face is less changed within frame size
+ decoder now has only 1 residual block instead of 2, result is same quality with less decoder size
+ added mid-full face, which covers 30% more area than half face.
+ added option " Enable 'true face' training "
Enable it only after 50k iters, when the face is sharp enough.
the result face will be more like src.
The most src-like face with 'true-face-training' you can achieve with DF architecture.
fixed model sizes from previous update.
avoided bug in ML framework(keras) that forces to train the model on random noise.
Converter: added blur on the same keys as sharpness
Added new model 'TrueFace'. This is a GAN model ported from https://github.com/NVlabs/FUNIT
Model produces near zero morphing and high detail face.
Model has higher failure rate than other models.
Keep src and dst faceset in same lighting conditions.
Session is now saved to the model folder.
blur and erode ranges are increased to -400+400
hist-match-bw is now replaced with seamless2 mode.
Added 'ebs' color transfer mode (works only on Windows).
FANSEG model (used in FAN-x mask modes) is retrained with new model configuration
and now produces better precision and less jitter
With interactive converter you can change any parameter of any frame and see the result in real time.
Converter: added motion_blur_power param.
Motion blur is applied by precomputed motion vectors.
So the moving face will look more realistic.
RecycleGAN model is removed.
Added experimental AVATAR model. Minimum required VRAM is 6GB (NVIDIA), 12GB (AMD)
Usage:
1) place data_src.mp4 10-20min square resolution video of news reporter sitting at the table with static background,
other faces should not appear in frames.
2) process "extract images from video data_src.bat" with FULL fps
3) place data_dst.mp4 video of face who will control the src face
4) process "extract images from video data_dst FULL FPS.bat"
5) process "data_src mark faces S3FD best GPU.bat"
6) process "data_dst extract unaligned faces S3FD best GPU.bat"
7) train AVATAR.bat stage 1, tune batch size to maximum for your card (32 for 6GB), train to 50k+ iters.
8) train AVATAR.bat stage 2, tune batch size to maximum for your card (4 for 6GB), train to decent sharpness.
9) convert AVATAR.bat
10) converted to mp4.bat
updated versions of modules
