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Here's an image of the previous hot dog blended with the previous Mr. Here is the apple and orange blended together as seen in lecture: The smoothness is a result of incorporating the smoothed mask at each level into the summation between the laplacians for image1 and image2 at that particular level. Adding the blended laplacians together yields the smoothly blended images you can see below. Then, at each level, I created a blended laplacian stack from the equation Lblend = Gmask*(L1)+(1-Gmask)*(L2). In addition, I created a mask that consisted of half zeroes and half ones divided vertically, and created a gaussian stack for the mask.
![gmask solver gmask solver](https://blog.kakaocdn.net/dn/bXOwIu/btqZUVzmjFy/rf5MIPoCuLocRtJ4RjFdz1/img.jpg)
In multiresolution blending, I created a blended image by creating gaussian and laplacian stacks for two images I'll call image1 and image2. In the Laplacian stacks, however, the girl's outline becomes much more visible and you can no longer see Abraham Lincoln. You can see that repeatedly applying the Gaussian makes the image more blurry as we increasingly smooth the image, and makes Abraham Lincoln more prominent and the girl in the image less noticeable. Here are the results when applying a Gaussian stack and Laplacian stack with 5 levels each on the Dali painting: In this part, I implemented Gaussian and Laplacian stacks by recursively applying either a Gaussian kernel or recursively performing the procedure for the Laplacian (Image - Guassian(Image)). This next one I consider a failure, since the shapes of the faces of Peter Kavinsky/Noah Centineo do not align well with that of a puppy:įor reference, here are the original images: Potato Head when he was a cucumber and a hot dog hybridized, and Mark Ruffalo and the Hulk hybridized together. The frequency analysis shown is for this image. This is Peter Kavinsky hybridized with Iron Man, two of my favorite people. In this part, I created hybrid images by low-pass filtering one image with the Gaussian, and high-pass filtering another image with the Laplacian, and then combining the two images together. To obtain the sharpened image, I then added the detail to the original image. Then, I subtracted the smoothed image from the original image to get the detail. To create the sharpened effect, I applied a Gaussian kernel on the original dogs image. The image I showed to sharpen is of 2 dogs. Macros for re-projection also allow artists to quickly place cleaned up ‘patches’ or new elements into a scene.CS 194-26 Project 3: Frequencies & Gradients Jennifer Liu (cs194-26-aag) This toolset allows Flame artists to place 3D objects in a moving shot and apply masking for compositing, selective effects or color grading. With machine learning, it discards moving ‘bad data’ like humans, vehicles, and sky regions that would otherwise have to be manually removed from a standard scene-based solve.
Gmask solver manual#
The Camera Analysis node can operate in a manual or all-automatic mode to solve camera field of view (FOV).
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Combining Structure from Motion (SfM) and visual simultaneous localization and mapping (SLAM) techniques, this new node produces thousands of accurately tracked points, enabling high-quality results in minutes. This new, next-generation camera tracker uses cutting-edge scene reconstruction algorithms similar to autonomous vehicle smart ‘vision’ and reality capture point cloud reconstruction. Flame’s new Camera Analysis tool provides busy VFX artists with remarkable, automatic camera solves and 3D geometry output.