Data Structure ?
2.23G
Data Structure ?
*The above analysis is the result extracted and analyzed by the system, and the specific actual data shall prevail.
README.md
学会した顔の3D表現は、コンピュータビジョンの問題（例えば、画像からの3D顔追跡と再構築）やグラフィックアプリケーション（例えば、キャラクター生成とアニメーション）に役立ちます。従来のモデルは、線形部分空間または高階テンソルの一般化を使用して、顔の潜在的な表現を学習します。この線形性のため、極端な変形や非線形な表情を捉えることができません。この問題を解決するために、私たちは、メッシュ表面上のスペクトル畳み込みを使用して、顔の非線形表現を学習する汎用モデルを導入しました。私たちは、階層的なメッシュ表現を可能にするメッシュサンプリング操作を導入しました。これにより、モデル内の複数のスケールで形状と表情の非線形変化を捉えることができます。変分設定では、私たちのモデルは多次元ガウス分布から様々なリアルな3D顔をサンプリングします。私たちの訓練データは、12の異なる被験者で捕捉された20,466個の極端な表情メッシュを含んでいます。訓練データが限られているにもかかわらず、私たちの訓練モデルは最新の顔モデルを上回り、再構築誤差が50％減少し、パラメータが75％減少しました。また、私たちのオートエンコーダで既存の最新の顔モデルの表情空間を置き換えることで、再構築誤差を減らすことができることを示しました。
データセットの参照
ここに、あなたの研究でCOMAを引用するためのBibtex断片を示します。
@inproceedings{COMA:ECCV18,
title = {畳み込みメッシュオートエンコーダを用いた3D顔の生成},
author = {Ranjan, Anurag および Bolkart, Timo および Sanyal, Soubhik および Black, Michael J.},
booktitle = {欧州コンピュータビジョン会議（ECCV）},
pages = {725--741},
year = {2018}
url = {http://coma.is.tue.mpg.de/}
}
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