Re: Dota 1 Hero Models
Icaro Aveiga
I'm getting into 3D printing and have gotten inspired by all the 3D-printed Dota heroes posted here. I have pretty much zero understanding of how the workshop works, so any knowledge about this is welcome!
The goal of the Model Pictures project is to create a collection of pictures that show the in-game models of the heroes of Dota 2.
All pictures are made through the use of the Counter Strike:Global Offensive SDK's model viewer, until such time that Dota 2 has its own SDK and we can start taking pictures in there. The current SDK, however, does not support particle effects, which in some cases leads to a noticeably different looking hero, with Morphling, Enigma and Wisp being most prominent.
A guide to hooking up Dota 2 models to the CS:GO SDK can be found here
All feedback and discussions go to the talk page so that this page will remain clear for the pictures and potentially expanded explanation on this page. When you give feedback on one or more pictures, please be precise in what you dislike.
8. The hero might not readily printable. Certain objects might be too thin, such as the britches here, and need to be thickened. Select the object and toggle to Edit Mode. Use the Shrink/Flatten transformation to thicken the object.
10. Some parts of the hero are only a single layer of faces. These need to be actual closed objects, with a certain thickness. Select all of relevant faces and apply an Extrude Region. This will create new faces in a different direction.
12. Export model for 3D printing. For a non-colored print, export the hero as an STL. For a colored print, the file format varies. If you are using an online 3D printing service, VRML2 is a good bet. You need to create a zip of the VRML2 file along with the PNG textures to upload to those printing services.
Hello Jenny, thank you very much for your guide. I suppose that the described way to acquire the mdl files does not work anymore in Source 2, as no .mdl files exist in the directories.
I can find a hero model on , but can you help me with how I can animate this .fbx model? The files include a .ma file as well but I dont know how to open that.
Best regards, Frederik
For the textures use a suffix, for example alchemist_head_color.tga. Use the TGA format and the texture resolution of the hero requirement page.Here are all the suffixes in the same order than the Workshop settings:
Now do a right click on the heroe name followed by a 1, it is the first line and select Export > Animation. This will export your model in his default pose with his armature, you can name the file heroename_rig_fix for example.
Select every meshes of the heroe and in the modifier tab assign the armature, if the mesh doesn't move it means the armature is oriented corectly. In Pose Mode the character should move with the rigging.
One of the coolest results in natural language processing is the success of word embedding models like Word2vec. These models are able to extract rich semantic information from words using surprisingly simple models like CBOW or skip-gram. What if we could use these generic modelling strategies to learn embeddings for something completely different - say, Dota 2 heroes.
One hypothesis we might have about a good hero embedding is that it should be able to learn what heroes typically fulfill which roles on a team. This makes sense from a Word2vec perspective because if our goal is to predict the last hero a team will pick, one thing that will help is to consider how many carries or supports the team has already picked. For example, if a team already has a lot of carries, they will almost certainly pick a support next.
In order to test this, we will set up a function to color-code heroes according to their role (as annotated in the OpenDota API). We will color supports in blue, carries in red, and heroes that are annotated as both in purple. Heroes that have neither annotation will be gray.
Using this it looks like it should be possible to loop over many match_ids from the table of recent pro matches, then extract the hero ID's (sid) from each individual match result. This is a bit annoying, because to train our model we will want a lot of data, and if we get it this way we will be making a separate call to the OpenDota API for each match we want to add to our dataset. Is there a better way?
As a test, we can manually parse out the first row of the results returned by our SQL query and show that we can successfully get the 5 hero IDs (sid) for the heroes picked by one of the teams in one match:
In practice, the vocabulary size for our Dota 2 hero embedding model is very small, since there are only about $10^2$ Dota heroes, so we don't expect this to be an issue. Just for fun though, we will implement this anyway.
Finally, one last quirk some people add to their Word2vec models is the constraint $W'=W^T$ - some people refer to this as a "shared embedding". It's not clear to me why this is a good inductive bias to add to Word2vec, but it certainly reduces the number of model parameters by half.
For each example we will want a target (the hero that was actually picked), a context (the heroes that have been picked so far), a negative (N_NEG heroes chosen at random from the heroes still in the pool to match the marginal popularity distribution of all heroes), and corresponding labels $l_i$ for the target and negative heroes (1 for the target because it is "correct" and 0 for the negative heroes because they are incorrect).
Once we have the hero picks set up as a Sequence, we can wire everything together in a CBOW model. We will use a Keras Embedding layer to convert hero IDs (an integer index) into vectors. The parameters of this layer are the weight matrix $W$ in our discussion above. Under the CBOW model, we can average (this is better than summing because we've set $W'=W^T$ so there's no flexibility to absorb the scaling constant) the vectors of the context heroes together to obtain $h$, which we will call cbow in the code. We can then compute the dot product between this average and the embeddings of the target or negative heroes to obtain the Negative Sampling logits $y$, which we call target_context_product and negative_context_product in the code. We can then compare these dot products to the labels $l_i$ using binary cross-entropy.
I know that there's a "Missing" command in the Chat Wheel that will report your lane missing, but that only does "Missing Top!" or "Missing Middle!" messages it seems. I've seen people call specific heroes missing before though:
After browsing reddit for memes, the esports team stumbled upon a thread about model evolutions for Dota heroes. Posted by Reddit user fakayubariza, the full list had several Dota strength heroes side-by-side with their models from each game they were a part of. We thought it would be fun to rank the worst and the best model changes for these heroes from Dota 1 to Dota 2!
Blizzard is stepping very differently than Dota in the world of character models in that they are altering even base models to be very diverse in appearance with the implantation of their own micro-transaction program. Their hero models will essentially have very little limitations on what they can do and be more in line with Heroes of Newerth and League of Legends as opposed to Dota 2.
In Roshpit Champions there are 30 Unique Heroes with Custom Skill Trees, using Dota 2 heroes models. Most of the heroes inherit main attributes, base attack time and attack animations from Dota 2 heroes.
"Supporting" part usually comes from a hero's rune, or their interaction with other items that allow them to help their allies. Example: Duskbringer E4 (temporally immortality effect) combine with his Immortal Glyph, can affect allies without having them actually died when the buff vanished; Crest of the Umbral Sentinel Ruby gem can decrease enemies stats, making the fight easier; etc.
Auriun can be considered as a full support hero despite his overwhelming damage output power, because he has excellent abilities in term of supporting: R - resurrection of fallen teammates, Q+Q2 shields which negate incoming damage on unit, W+W3 which heals with high tick speed, W4 which adds miss chance for enemies, Q3 which buffs ally armor after shield expires, E4 that freeze enemies around him on cast of E.
Games have historically been one of the main arenas to test the progress of artificial intelligence algorithms. Initial efforts involved developing AI models that could play board games such as chess and checkers, and eventually the complicated Chinese game of Go. More recently, AI researchers have turned their attention toward video games, which are significantly more complicated and challenging.
OpenAI Five is a team of five neural networks (hence the name), one for each of the five hero characters in a team. Neural networks are software constructions that develop their behavior by analyzing large data sets and finding correlations and patterns.
Out of the 117 different characters available in the game, OpenAI limited the competition to 17 characters. Given that each game involves ten heroes, this reduces the number of possibilities from approx. 89 trillion (117 choose 10) to 19,448 (17 choose 10).
Dota 2 skins are special cosmetic items that help customize the visual elements within the game. These can help you completely alter the look and feel of your Dota 2 hero or character. However, unlike with most other games, Dota 2 skins are purely graphical changes that do not affect the game's mechanics or gameplay. They do not in any way add or change the abilities or stats of a character in-game. The changes are only visual.
That said, a few items do, in fact, add custom effects to the hero or their abilities, but they are only cosmetic changes. Characters are not stronger or weaker depending on the Dota 2 skins that are equipped on them.
Skins found in the Legendary, Immortal, and Arcana rarity usually come with added custom effects that change the way different hero abilities look, alter the icons, and add particle effects to make the hero stand out even more. Hence, they are some of the most sought-after skins.
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