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$16 for a *very* non-performant material? If this was intended for use in high-detail scenes, not meant for gameplay, one would generally just use a flipbook animation, or looping HD video texture (both of which are higher quality and available for free all over). I love options, but c'mon, that's pretty steep. $5, maybe. And you can loop in materials, using custom HLSL nodes. Also, there are better ways of doing this, all around. Somewhere on the forums, Ryan Brucks (of Epic fame) himself touched on this. I've personally been working on a cool water material (not "material blueprint", thankyouverymuch) and utility functions, and am close to the quality achieved here, sitting at ~180 instructions with everything "turned on". The kicker? It's pure procedural. No textures are needed. So this is cool, no doubt about that. In my humble opinion though, it's not "good". It doesn't run fast, and it's more complicated than it needs to be.
The team recorded over eight hours of audio and video of a speaker reciting more than 2500 different sentences. The speaker’s face was tracked and the data was used to create a reference face for an animation model. Then they used special to transcribe the speech sounds. This whole process trained a neural network to animate a reference face, frame-by-frame, based on phonemes.
Training the AI is said to take only a couple of hours, letting specialists use speech from any speaker with any accent and even in different languages. The method is also capable of dealing with the singing.
We introduce a simple and effective deep learning approach to automatically generate natural looking speech animation that synchronizes to input speech. Our approach uses a sliding window predictor that learns arbitrary nonlinear mappings from phoneme label input sequences to mouth movements in a way that accurately captures natural motion and visual coarticulation effects. Our deep learning approach enjoys several attractive properties: it runs in real-time, requires minimal parameter tuning, generalizes well to novel input speech sequences, is easily edited to create stylized and emotional speech, and is compatible with existing animation retargeting approaches. One important focus of our work is to develop an effective approach for speech animation that can be easily integrated into existing production pipelines. We provide a detailed description of our end-to-end approach, including machine learning design decisions. Generalized speech animation results are demonstrated over a wide range of animation clips on a variety of characters and voices, including singing and foreign language input. Our approach can also generate on-demand speech animation in real-time from user speech input.