Fix smoothing errors in 3ds max 8
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- FIX SMOOTHING ERRORS IN 3DS MAX 8 SKIN
- FIX SMOOTHING ERRORS IN 3DS MAX 8 FULL
- FIX SMOOTHING ERRORS IN 3DS MAX 8 CODE
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In the third state, all faces are assigned to one smoothing group. Checkbox "Auto Smooth Mesh" has three states.
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When you export the head or the head parts be set checkbox "Head or Head part" Import and export of most models SSE, including models of head and head parts. In the Export window, set the normal export and tangentsov for all other meshes. We put in the properties of the body normal = 0, thus disabling the normal export only for this mesh. What is it for? For example, in the scene we have the armor and body fragment. Set can be the following:Īccordingly, the values ?may be zero. In the object properties of some export options can be set, which will have priority over the general parameters set by the export window.
FIX SMOOTHING ERRORS IN 3DS MAX 8 SKIN
Fixed a bug with the violation of the UVs, and skin while exporting the model in the case of automatic partitioning thereof to smoothing groups. "Normals" checkbox uses for Skyrim, Skyrim Special Edition and Fallout 4 Checkboxes "Auto Smooth Mesh" "Vertex Colors" are divided into two by two, because in max 21 an incorrect checkbox with three states
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Automatic profile definition for some games For collisions settings added parameter "Solver Deactivation"
FIX SMOOTHING ERRORS IN 3DS MAX 8 FULL
Added helper bhkCollisionObject with full functionality for collision settings Removed bhkRigidBody, bhkCollProxy, bhkBox, bhkSphere, bhkCapsule, bhkRigidBodyModifier and util NifProps compile and decompile bhkCompressedMeshShape Removed NifMopp.exe, NifMopp.dll and NifMagic.dll Added new setting "Root Node Name" in to export dialog Fix export flags SLSF1_FACEGEN_DETAIL_MAP, SLSF1_FACEGEN_RGB_TINT, SLSF2_BACK_LIGHTING and SLSF1_MODEL_SPACE_NORMALS for SkinTint and FaceTint shaders Fix export skinned mesh if skeleton hidden Fix export vertex color for Skyrim profile and flags SLSF2_VERTEX_COLORS and SLSF1_VERTEX_ALPHA If you need any more explanation please comment below.This is an beta version of the plugin, and are designed to test for the purpose of further development. This is done like so- b_conv1 = bias_variable(). For example, if W_conv1 = weight_variable() appears like so, you take the final number and put that into your bias variable so it can match the dimensions of the input. Once again, these numbers correspond to the dimension of your feature tensor.įor every bias variable, you choose the final number of the previously defined variable. When you reshape your x_foo tensor (I call it x_ ), you, for whatever reason, have to define it like so- x_ = tf.reshape(x, ) Notice the correspond to the dimensions of the input and output tensors/predictors. You're going to have to make the first 2 numbers conform to the feature tensor that you are using to train your model, the last two numbers will be the dimension of the predicted output (same as the dimension of the input).
FIX SMOOTHING ERRORS IN 3DS MAX 8 CODE
When you're defining your weight variable (see code below)- def weight_variable(shape): This will match an output to a 1 dimensional input and prevent errors down the road. Notice how I changed the strides and the ksize to. When you're dealing with- def conv2d(x, W): If you input is length 1, your output should be length 1 (length is substituted for dimension). You have to shape the input so it is compatible with both the training tensor and the output. What I'm trying to get is a probability in the empty column that the column will equal a 1. Just for some background information, the data that I'm dealing with is a CSV file where each row contains 10 features and 1 empty column that can be a 1 or a 0. ValueError: ('filter must not be larger than the input: ', 'Filter: ', 'Input: ') I get the following errors: ValueError Traceback (most recent call last)Ĥ h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) H_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)Īnd then when I try to run this command: W_conv2 = weight_variable() Return tf.nn.conv2d(x, W, strides=, padding='SAME') Initial = tf.truncated_normal(shape, stddev=0.1) I'm trying to apply the expert portion of the tutorial to my own data but I keep running into dimension errors.