Franker face z
Author: C | 2025-04-25
This tutorial shows how to spam emotes more easily on Twitch using a Chrome extension called Franker Face Z (FFZ). It's straightforward to set up and I will This tutorial shows how to spam emotes more easily on Twitch using a Chrome extension called Franker Face Z (FFZ). It's straightforward to set up and I will
What is franker z? : r/OutOfTheLoop - Reddit
2) is "the value of variable S divided by 2", where S is the length of the sides of the cube.Likewise, the back face has to be translated by +S/2 in the Z direction, after being rotated 180 degrees along the Y axis:.face.back { transform: rotateY(180deg) translateZ(calc(var(--S) / 2));}Why the rotateY, you ask? Well, the faces of the cube hae a back and a front themselves. The "front" should point away from the center of the cube. If we just move the back face behind the screen (-S/2 in the Z direction), its front will point to the center. That's why we rotate it along the vertical axis (which is the Y axis) to basically flip it around. One difficult thing here is, that after the rotation, the axes for the face have changed! The Z axis is now also 'flipped', so we have to do the same translation over +S/2 (and not -S/2!) in the Z direction.So how about the left and right faces then? They should both be rotated 90 degrees along that same vertical axis: the left face over -90 and the right over +90 degrees. After this, we have to pull the left face towards the left and the right face towards the right. For the left face, which was turned to the left, the Z axis now runs left to right. To move it left, we have to pull it out of the screen again, which is also positive Z. The right face has to undergo the same translation, because by turning it left, the Z axis is turned to left to right as well, but in the other direction. Anyway, this becomes:.face.left { transform: rotateY(-90deg) translateZ(calc(var(--S) / 2));}.face.right { transform: rotateY(90deg) translateZ(calc(var(--S) / 2));}Top and bottom are similar, of course, only they rotate along
7TV, BTTV, Franker emotes not showing in chat :
Sess, torch.randn(5, 160, 160, 3).detach()))Distance 1.2874517096861382e-06">Passing test data through TF modeltensor([[-0.0142, 0.0615, 0.0057, ..., 0.0497, 0.0375, -0.0838], [-0.0139, 0.0611, 0.0054, ..., 0.0472, 0.0343, -0.0850], [-0.0238, 0.0619, 0.0124, ..., 0.0598, 0.0334, -0.0852], [-0.0089, 0.0548, 0.0032, ..., 0.0506, 0.0337, -0.0881], [-0.0173, 0.0630, -0.0042, ..., 0.0487, 0.0295, -0.0791]])Passing test data through PT modeltensor([[-0.0142, 0.0615, 0.0057, ..., 0.0497, 0.0375, -0.0838], [-0.0139, 0.0611, 0.0054, ..., 0.0472, 0.0343, -0.0850], [-0.0238, 0.0619, 0.0124, ..., 0.0598, 0.0334, -0.0852], [-0.0089, 0.0548, 0.0032, ..., 0.0506, 0.0337, -0.0881], [-0.0173, 0.0630, -0.0042, ..., 0.0487, 0.0295, -0.0791]], grad_fn=)Distance 1.2874517096861382e-06In order to re-run the conversion of tensorflow parameters into the pytorch model, ensure you clone this repo with submodules, as the davidsandberg/facenet repo is included as a submodule and parts of it are required for the conversion.ReferencesDavid Sandberg's facenet repo: Schroff, D. Kalenichenko, J. Philbin. FaceNet: A Unified Embedding for Face Recognition and Clustering, arXiv:1503.03832, 2015. PDFQ. Cao, L. Shen, W. Xie, O. M. Parkhi, A. Zisserman. VGGFace2: A dataset for recognising face across pose and age, International Conference on Automatic Face and Gesture Recognition, 2018. PDFD. Yi, Z. Lei, S. Liao and S. Z. Li. CASIAWebface: Learning Face Representation from Scratch, arXiv:1411.7923, 2014. PDFK. Zhang, Z. Zhang, Z. Li and Y. Qiao. Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks, IEEE Signal Processing Letters, 2016. PDF[GUIDE] Franker Facez Compressorsettings : r/Twitch - Reddit
To rotate the target by specifying a numerical angle.It is primarily used on the neck, arms, and legs to create a leaning motion.Create a Face-Tilting MovementThe tilting motion of the face is called [Angle Z] in the Live2D parameters.First, place the head part in a rotation deformer.From the Parts palette, lock the neck, arms, body, and legs.Once locked, use Ctrl + A to select all ArtMeshes that are not locked.With those ArtMeshes selected, click [Create Rotation Deformer] at the top of the screen.Clicking on [Create Rotation Deformer] will bring up a new dialog box.The Name field can be set as desired.You can use the list to select the part in which to insert the deformer. For this example, select a face since it is a face rotation.Be sure to select “Set as Parent of Selected Object” option for Addition Destination.After confirming the various settings, click [Create].The rotation deformer is now ready.When rotated, the selected face parts will rotate.If any parts are missing, they can be inserted by selecting the ArtMesh and then selecting Deformer from the Inspector.Deformers can be positioned by holding down Ctrl and moving them. This operation is similar for warp deformers.Make sure to hold down Ctrl; otherwise the ArtMesh inside will move with it.Move the rotation deformer to the chin position.Once the position has been adjusted, the parameters are used to add movement.The procedure for attaching parameters is the same as for an ArtMesh.Add three keys to the face rotation Z parameter to add motion. Here, each side should move 10 degrees.Add Hair Swing MovementNext, let’s create the hair swing movement. Movement is applied to the front, sides, and back of the hair, respectively.First select the bangs ArtMesh and click on [Create Warp Deformer] in the center of the menu above.As with the rotation deformer, a dialog. This tutorial shows how to spam emotes more easily on Twitch using a Chrome extension called Franker Face Z (FFZ). It's straightforward to set up and I willGen Z’s scrunch face replaces millennials’ duck face
J.M.; Marcel, S. LBP – TOP based countermeasure against face spoofing attacks. In Proceedings of the ACCV’ 12 Proceedings of the 11th International Conference on Computer Vision–Volume Part I, Daejeon, Korea, 5–6 November 2012; pp. 121–132. [Google Scholar]Bharadwaj, S.; Dhamecha, T.I.; Vatsa, M.; Singh, R. Computationally Efficient Face Spoofing Detection with Motion Magnification. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Portland, OR, USA, 23–28 June 2013; pp. 105–110. [Google Scholar] [CrossRef]Tang, D.; Zhou, Z.; Zhang, Y.; Zhang, K. Face Flashing: A Secure Liveness Detection Protocol Based on Light Reflections. arXiv 2018, arXiv:1801.01949. [Google Scholar]Yeh, C.; Chang, H. Face Liveness Detection Based on Perpetual Image Quality Assessment Features with Multi-Scale Analysis. In Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 12–15 March 2018; pp. 49–56. [Google Scholar] [CrossRef]Pan, S.; Deravi, F. Spatio-Temporal Texture Features for Presentation Attack Detection in Biometric Systems. In Proceedings of the 2019 Eighth International Conference on Emerging Security Technologies (EST), Colchester, UK, 22–24 July 2019; pp. 1–6. [Google Scholar] [CrossRef] [Green Version]Asim, M.; Ming, Z.; Javed, M.Y. CNN based spatio-temporal feature extraction for face anti-spoofing. In Proceedings of the 2017 2nd International Conference on Image, Vision, and Computing (ICIVC), Chengdu, China, 2–4 June 2017; pp. 234–238. [Google Scholar] [CrossRef]Xu, Z.; Li, S.; Deng, W. Learning temporal features using LSTM-CNN architecture for face anti-spoofing. In Proceedings of the 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, Malaysia, 3–6 NovemberHow to Resign from Amazon (Using A to Z or Face-to-Face and
Another face or edge to define the X axis. Both the Z and X axes can be flipped 180 degrees.Select Z axis/plane & Y axis - Select a face or an edge to define the Z axis and another face or edge to define the Y axis. Both the Z and Y axes can be flipped 180 degrees.Select X & Y axes - Select a face or an edge to define the X axis and another face or edge to define the Y axis. Both the X and Y axes can be flipped 180 degrees.Select coordinate system - Sets a specific tool orientation for this operation from a defined user coordinate system in the model. This uses both the origin and orientation of the existing coordinate system. Use this if your model does not contain a suitable point & plane for your operation.The Origin drop-down menu offers the following options for locating the triad origin:Setup WCS origin - Uses the workpiece coordinate system (WCS) origin of the current setup for the tool origin.Model origin - Uses the coordinate system (WCS) origin of the current part for the tool origin.Selected point - Select a vertex or an edge for the triad origin.Stock box point - Select a point on the stock bounding box for the triad origin.Model box point - Select a point on the model bounding box for the triad origin. Heights tab settingsClearance HeightThe Clearance height is the first height the tool rapids to on its way to the start of the tool path. Clearance HeightRetract height: incremental offset from the Retract Height.Feed height: incremental offset from the Feed Height.Top height: incremental offset from the Top Height.Bottom height: incremental offset from the Bottom Height.Model top: incremental offset from the Model Top.Model bottom: incremental offset from the Model Bottom.Stock top: incremental offset from the Stock Top.Stock bottom: incremental offset from the Stock Bottom.Selected contour(s): incremental offset from a Contour selected on the model.Selection: incremental offset from a Point (vertex), Edge or Face selected on the model.Origin (absolute): absolute offset from the Origin that is defined in either the Setup orGen Z s scrunch face replaces millennials duck face as
Dimensions. Place the y-z coordinate axis center point in the middle of the cross section. Define the y and z coordinate directions. For example: positive y axis: up negative y axis: down positive z axis: on the right negative z axis: on the left Define the y and z vectors. For example: y1, y2, y3 z1, z2, z3 Make coordinate pairs for the points. Assign y, z vector pairs to each point. Start from the lower right corner and define the points in counterclockwise order. For example: point 1: y1 z3 point 2: y2 z3 point 3: y3 z2 point 4: y3 z1 point 5: y1 z1 Create the .clb file After defining the shape and point coordinates of the profile, continue by creating the .clb file. Create a new .clb file using any standard text editor, such as Microsoft Notepad. Define a library name to be used in the profitab.inp file for this profile. For example: library_id "1Gen" Define a cross section name to be used in the profitab.inp file for this profile. For example: Section_type{name "RectChamfer" Define the dimensions of the cross section. For example: base_attribute{name "h"description "albl_Height"type dimensiondefault 1000} Define the coordinates of the profile. The coordinates must be the same as the y and z vectors that you defined earlier. Define the default values. For example: expression{ name "y1"type y default -400formula -h/2} Define the geometry of one or several faces of the profile. For example: geometry{name "default"face { index 0 point 0 y1 z3 point 0 y2 z4 point 0 y3 z4 point 0 y4 z3 point 0 y4 z2 point 0 y3 z1 point 0 y2 z1 point 0 y1 z1 }face { index 1 point 1 y5 z7 point 1 y6 z8 point 1 y7 z8 point 1 y8 z7 point 1 y8. This tutorial shows how to spam emotes more easily on Twitch using a Chrome extension called Franker Face Z (FFZ). It's straightforward to set up and I willComments
2) is "the value of variable S divided by 2", where S is the length of the sides of the cube.Likewise, the back face has to be translated by +S/2 in the Z direction, after being rotated 180 degrees along the Y axis:.face.back { transform: rotateY(180deg) translateZ(calc(var(--S) / 2));}Why the rotateY, you ask? Well, the faces of the cube hae a back and a front themselves. The "front" should point away from the center of the cube. If we just move the back face behind the screen (-S/2 in the Z direction), its front will point to the center. That's why we rotate it along the vertical axis (which is the Y axis) to basically flip it around. One difficult thing here is, that after the rotation, the axes for the face have changed! The Z axis is now also 'flipped', so we have to do the same translation over +S/2 (and not -S/2!) in the Z direction.So how about the left and right faces then? They should both be rotated 90 degrees along that same vertical axis: the left face over -90 and the right over +90 degrees. After this, we have to pull the left face towards the left and the right face towards the right. For the left face, which was turned to the left, the Z axis now runs left to right. To move it left, we have to pull it out of the screen again, which is also positive Z. The right face has to undergo the same translation, because by turning it left, the Z axis is turned to left to right as well, but in the other direction. Anyway, this becomes:.face.left { transform: rotateY(-90deg) translateZ(calc(var(--S) / 2));}.face.right { transform: rotateY(90deg) translateZ(calc(var(--S) / 2));}Top and bottom are similar, of course, only they rotate along
2025-04-07Sess, torch.randn(5, 160, 160, 3).detach()))Distance 1.2874517096861382e-06">Passing test data through TF modeltensor([[-0.0142, 0.0615, 0.0057, ..., 0.0497, 0.0375, -0.0838], [-0.0139, 0.0611, 0.0054, ..., 0.0472, 0.0343, -0.0850], [-0.0238, 0.0619, 0.0124, ..., 0.0598, 0.0334, -0.0852], [-0.0089, 0.0548, 0.0032, ..., 0.0506, 0.0337, -0.0881], [-0.0173, 0.0630, -0.0042, ..., 0.0487, 0.0295, -0.0791]])Passing test data through PT modeltensor([[-0.0142, 0.0615, 0.0057, ..., 0.0497, 0.0375, -0.0838], [-0.0139, 0.0611, 0.0054, ..., 0.0472, 0.0343, -0.0850], [-0.0238, 0.0619, 0.0124, ..., 0.0598, 0.0334, -0.0852], [-0.0089, 0.0548, 0.0032, ..., 0.0506, 0.0337, -0.0881], [-0.0173, 0.0630, -0.0042, ..., 0.0487, 0.0295, -0.0791]], grad_fn=)Distance 1.2874517096861382e-06In order to re-run the conversion of tensorflow parameters into the pytorch model, ensure you clone this repo with submodules, as the davidsandberg/facenet repo is included as a submodule and parts of it are required for the conversion.ReferencesDavid Sandberg's facenet repo: Schroff, D. Kalenichenko, J. Philbin. FaceNet: A Unified Embedding for Face Recognition and Clustering, arXiv:1503.03832, 2015. PDFQ. Cao, L. Shen, W. Xie, O. M. Parkhi, A. Zisserman. VGGFace2: A dataset for recognising face across pose and age, International Conference on Automatic Face and Gesture Recognition, 2018. PDFD. Yi, Z. Lei, S. Liao and S. Z. Li. CASIAWebface: Learning Face Representation from Scratch, arXiv:1411.7923, 2014. PDFK. Zhang, Z. Zhang, Z. Li and Y. Qiao. Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks, IEEE Signal Processing Letters, 2016. PDF
2025-04-18J.M.; Marcel, S. LBP – TOP based countermeasure against face spoofing attacks. In Proceedings of the ACCV’ 12 Proceedings of the 11th International Conference on Computer Vision–Volume Part I, Daejeon, Korea, 5–6 November 2012; pp. 121–132. [Google Scholar]Bharadwaj, S.; Dhamecha, T.I.; Vatsa, M.; Singh, R. Computationally Efficient Face Spoofing Detection with Motion Magnification. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Portland, OR, USA, 23–28 June 2013; pp. 105–110. [Google Scholar] [CrossRef]Tang, D.; Zhou, Z.; Zhang, Y.; Zhang, K. Face Flashing: A Secure Liveness Detection Protocol Based on Light Reflections. arXiv 2018, arXiv:1801.01949. [Google Scholar]Yeh, C.; Chang, H. Face Liveness Detection Based on Perpetual Image Quality Assessment Features with Multi-Scale Analysis. In Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 12–15 March 2018; pp. 49–56. [Google Scholar] [CrossRef]Pan, S.; Deravi, F. Spatio-Temporal Texture Features for Presentation Attack Detection in Biometric Systems. In Proceedings of the 2019 Eighth International Conference on Emerging Security Technologies (EST), Colchester, UK, 22–24 July 2019; pp. 1–6. [Google Scholar] [CrossRef] [Green Version]Asim, M.; Ming, Z.; Javed, M.Y. CNN based spatio-temporal feature extraction for face anti-spoofing. In Proceedings of the 2017 2nd International Conference on Image, Vision, and Computing (ICIVC), Chengdu, China, 2–4 June 2017; pp. 234–238. [Google Scholar] [CrossRef]Xu, Z.; Li, S.; Deng, W. Learning temporal features using LSTM-CNN architecture for face anti-spoofing. In Proceedings of the 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, Malaysia, 3–6 November
2025-04-04Another face or edge to define the X axis. Both the Z and X axes can be flipped 180 degrees.Select Z axis/plane & Y axis - Select a face or an edge to define the Z axis and another face or edge to define the Y axis. Both the Z and Y axes can be flipped 180 degrees.Select X & Y axes - Select a face or an edge to define the X axis and another face or edge to define the Y axis. Both the X and Y axes can be flipped 180 degrees.Select coordinate system - Sets a specific tool orientation for this operation from a defined user coordinate system in the model. This uses both the origin and orientation of the existing coordinate system. Use this if your model does not contain a suitable point & plane for your operation.The Origin drop-down menu offers the following options for locating the triad origin:Setup WCS origin - Uses the workpiece coordinate system (WCS) origin of the current setup for the tool origin.Model origin - Uses the coordinate system (WCS) origin of the current part for the tool origin.Selected point - Select a vertex or an edge for the triad origin.Stock box point - Select a point on the stock bounding box for the triad origin.Model box point - Select a point on the model bounding box for the triad origin. Heights tab settingsClearance HeightThe Clearance height is the first height the tool rapids to on its way to the start of the tool path. Clearance HeightRetract height: incremental offset from the Retract Height.Feed height: incremental offset from the Feed Height.Top height: incremental offset from the Top Height.Bottom height: incremental offset from the Bottom Height.Model top: incremental offset from the Model Top.Model bottom: incremental offset from the Model Bottom.Stock top: incremental offset from the Stock Top.Stock bottom: incremental offset from the Stock Bottom.Selected contour(s): incremental offset from a Contour selected on the model.Selection: incremental offset from a Point (vertex), Edge or Face selected on the model.Origin (absolute): absolute offset from the Origin that is defined in either the Setup or
2025-04-14