this is the epic story of a group of talented digital artists trying to overcame daily technical challenges to achieve incredibly photorealistic projects of monsters and aliens
Arminas created this using Juggernaut Xl model and QR Code Monster SDXL ControlNet.
His pipeline: Static Images – Forge UI. Upscaled with Leonardo AI universal upscaler. Animated with Runway ML and Minimax. Video upscale – Topaz Video AI. Composited in Adobe Premiere.
A number of problems in computer vision and related fields would be mitigated if camera spectral sensitivities were known. As consumer cameras are not designed for high-precision visual tasks, manufacturers do not disclose spectral sensitivities. Their estimation requires a costly optical setup, which triggered researchers to come up with numerous indirect methods that aim to lower cost and complexity by using color targets. However, the use of color targets gives rise to new complications that make the estimation more difficult, and consequently, there currently exists no simple, low-cost, robust go-to method for spectral sensitivity estimation that non-specialized research labs can adopt. Furthermore, even if not limited by hardware or cost, researchers frequently work with imagery from multiple cameras that they do not have in their possession.
To provide a practical solution to this problem, we propose a framework for spectral sensitivity estimation that not only does not require any hardware (including a color target), but also does not require physical access to the camera itself. Similar to other work, we formulate an optimization problem that minimizes a two-term objective function: a camera-specific term from a system of equations, and a universal term that bounds the solution space.
Different than other work, we utilize publicly available high-quality calibration data to construct both terms. We use the colorimetric mapping matrices provided by the Adobe DNG Converter to formulate the camera-specific system of equations, and constrain the solutions using an autoencoder trained on a database of ground-truth curves. On average, we achieve reconstruction errors as low as those that can arise due to manufacturing imperfections between two copies of the same camera. We provide predicted sensitivities for more than 1,000 cameras that the Adobe DNG Converter currently supports, and discuss which tasks can become trivial when camera responses are available.
5.10 of this tool includes excellent tools to clean up cr2 and cr3 used on set to support HDRI processing.
Converting raw to AcesCG 32 bit tiffs with metadata.
In the retina, photoreceptors, bipolar cells, and horizontal cells work together to process visual information before it reaches the brain. Here’s how each cell type contributes to vision:
The primary goal of physically-based rendering (PBR) is to create a simulation that accurately reproduces the imaging process of electro-magnetic spectrum radiation incident to an observer. This simulation should be indistinguishable from reality for a similar observer.
Because a camera is not sensitive to incident light the same way than a human observer, the images it captures are transformed to be colorimetric. A project might require infrared imaging simulation, a portion of the electro-magnetic spectrum that is invisible to us. Radically different observers might image the same scene but the act of observing does not change the intrinsic properties of the objects being imaged. Consequently, the physical modelling of the virtual scene should be independent of the observer.
Ethan Roffler I recently had the honor of interviewing this VFX genius and gained great insight into what it takes to work in the entertainment industry. Keep in mind, these questions are coming from an artist’s perspective but can be applied to any creative individual looking for some wisdom from a professional. So grab a drink, sit back, and enjoy this fun and insightful conversation.
Ethan To start, I just wanted to say thank you so much for taking the time for this interview!
Daniele My pleasure. When I started my career I struggled to find help. Even people in the industry at the time were not that helpful. Because of that, I decided very early on that I was going to do exactly the opposite. I spend most of my weekends talking or helping students. ;)
Ethan That’s awesome! I have also come across the same struggle! Just a heads up, this will probably be the most informal interview you’ll ever have haha! Okay, so let’s start with a small introduction!
Note. The Median Cut algorithm is typically used for color quantization, which involves reducing the number of colors in an image while preserving its visual quality. It doesn’t directly provide a way to identify the brightest areas in an image. However, if you’re interested in identifying the brightest areas, you might want to look into other methods like thresholding, histogram analysis, or edge detection, through openCV for example.
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![[gamma correction test]](http://www.madore.org/~david/misc/color/gammatest.png "sRGB gamma correction test")
