Terrain Generation with Deep Learning
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Latest comments
by Vaidas
26 min ago

Technically, the artist needs to (and does) credit the author of the artwork he referenced and only mention what and where from the character is. Given that, this is a 3d/gaming/technical thingie-ma-jibs website that does not (and probably shouldn't really) reflect on the circumstance of the character itself, but concentrate on creation and techniques used in creation. The name of the character is referenced, but nowhere on the original art the name Sam Riegel is mentioned. As much as critter community is nice and welcoming, this part of "CREDIT THIS OR CREDIT THAT" irritates me. IMHO, Credit is given where credit is due. This 3d model was made with learning purposes only, whereas the original art is being sold. Instead of commenting "GIVE CREDIT" comment "COOL ART OF SAM'S CHARACTER" or "GREAT CRITICAL ROLE ART". All that said, this is an amazing rendition of the original artwork of the character of critical role. As a critter, I love both this piece and the idea of other critter being so talented! Peace, a member of the wonderful critter family.

by Amy
3 hours ago

You need to make it clear that this is an interpretation of someone else’s character and credit them (Sam Reigel, from Critical Role).

by Amy
3 hours ago

As great as this is, it’s not actually “your character” so you should really credit Sam Reigel of Critical Role who created this character, and make it clear this is your interpretation of it, because you make it sound like it was all your idea.

Terrain Generation with Deep Learning
9 October, 2017
News

Eric Guérin has recently presented a new model that makes an attempt to build complete terrain maps from a few user sketches. The thing can potentially change the way we deal with landscape generation, so let’s start studying it. 

The model was originally described in a paper called “Interactive Example-Based Terrain Authoring with Conditional Generative Adversarial Networks” by Eric Guérin, Julie Digne, Eric Galin, Adrien Peytavie, Christian Wolf, Bedrich Benes and Benoît Martinez. You can read about the idea below.

Abstract 

Authoring virtual terrains presents a challenge and there is a strong need for authoring tools able to create realistic terrains with simple user-inputs and with high user control. We propose an example-based authoring pipeline that uses a set of terrain synthesizers dedicated to specific tasks. Each terrain synthesizer is a Conditional Generative Adversarial Network trained by using real-world terrains and their sketched counterparts. The training sets are built automatically with a view that the terrain synthesizers learn the generation from features that are easy to sketch. During the authoring process, the artist first creates a rough sketch of the main terrain features, such as rivers, valleys and ridges, and the algorithm automatically synthesizes a terrain corresponding to the sketch using the learned features of the training samples. Moreover, an erosion synthesizer can also generate terrain evolution by erosion at a very low computational cost. Our framework allows for an easy terrain authoring and provides a high level of realism for a minimum sketch cost. We show various examples of terrain synthesis created by experienced as well as inexperienced users who are able to design a vast variety of complex terrains in a very short time.

Get the full text

Source: LinkedIn

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