- 3D object reconstruction from a single image is a highly under-determined problem, requiring strong prior knowledge of plausible 3D shapes.
- In this work, we propose an end-to-end trainable framework, sequentially estimating 2.5D sketches and 3D object shapes.
- First, compared to full 3D shape, the 2.5D sketch is much easier to be recovered from the 2D image, and to transfer from synthetic to real images.
- Second, for 3D reconstruction from the 2.5D sketches, we can easily transfer the learned model on synthetic data to real images, as rendered 2.5D sketches are invariant to object appearance variations in real images, including lighting, texture, etc.
- Third, we derive differentiable projective functions from 3D shapes to 2.5D sketches, making the framework end-to-end trainable on real images, requiring no real-image annotations.
- It’s all about ‘style transfers’ in ‘Come Swim,’ her latest short film.
- Stewart and her team used style transfers to create some unusual, dream-like sequences in the film.
- Stewart’s paper, co-authored with special effects engineer Bhautik J Joshi and producer David Shapiro, was released through the online repository arXiv.
- At its core, the system relies on deep neural networks to identify the “content” of your photo and the “style” of another, blending them together into a completely new image.
- It details her use of a technique known as ‘style transfers’ for select scenes in Come Swim , a short film that will be shown at Sundance and marks her directorial debut.
Kristen Stewart, best known for her role as Bella in the Twilight saga, has co-authored a paper on machine learning. It details her use of a technique known as…
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