We develop a Learning Direct Optimization (LiDO) method for the refinement of
a latent variable model that describes input image x. Our goal is to explain a
single image x with an interpretable 3D computer graphics model having scene
graph latent variables z (such as object appearance, camera position). Given a
current estimate of z we can render a prediction of the image g(z), which can
be compared to the image x. The standard way to proceed is then to measure the
error E(x, g(z)) between the two, and use an optimizer to minimize the error.
However, it is unknown which error measure E would be most effective for
simultaneously addressing issues such as misaligned objects, occlusions,
textures, etc. In contrast, the LiDO approach trains a Prediction Network to
predict an update directly to correct z, rather than minimizing the error with
respect to z. Experiments show that our LiDO method converges rapidly as it
does not need to perform a search on the error landscape, produces better
solutions than error-based competitors, and is able to handle the mismatch
between the data and the fitted scene model. We apply LiDO to a realistic
synthetic dataset, and show that the method also transfers to work well with
real images.

本文提出了一种名为 Learning Direct Optimization 的方法，用于解释和优化具有潜变量和场景图潜变量的 3D 计算机图形模型，实验表明，该方法比传统的以误差为基础的竞争方法更有效，能够更好地处理数据和拟合场景模型之间的不匹配问题。