“We combine these two optical systems in a single camera by splitting the aperture: one half applies application-specific modulation using a diffractive optical element, and the other captures a conventional image. This co-design with a dual-pixel sensor allows simultaneous capture of coded and uncoded images — without increasing physical or computational footprint.”
The EU Artificial Intelligence (AI) Act, which went into effect on August 1, 2024.
This act implements a risk-based approach to AI regulation, categorizing AI systems based on the level of risk they pose. High-risk systems, such as those used in healthcare, transport, and law enforcement, face stringent requirements, including risk management, transparency, and human oversight.
Key provisions of the AI Act include:
Transparency and Safety Requirements: AI systems must be designed to be safe, transparent, and easily understandable to users. This includes labeling requirements for AI-generated content, such as deepfakes (Engadget).
Risk Management and Compliance: Companies must establish comprehensive governance frameworks to assess and manage the risks associated with their AI systems. This includes compliance programs that cover data privacy, ethical use, and geographical considerations (Faegre Drinker Biddle & Reath LLP) (Passle).
Copyright and Data Mining: Companies must adhere to copyright laws when training AI models, obtaining proper authorization from rights holders for text and data mining unless it is for research purposes (Engadget).
Prohibitions and Restrictions: AI systems that manipulate behavior, exploit vulnerabilities, or perform social scoring are prohibited. The act also sets out specific rules for high-risk AI applications and imposes fines for non-compliance (Passle).
For US tech firms, compliance with the EU AI Act is critical due to the EU’s significant market size
FLUX (or FLUX. 1) is a suite of text-to-image models from Black Forest Labs, a new company set up by some of the AI researchers behind innovations and models like VQGAN, Stable Diffusion, Latent Diffusion, and Adversarial Diffusion Distillation
Depth of field is the range within which focusing is resolved in a photo.
Aperture has a huge affect on to the depth of field.
Changing the f-stops (f/#) of a lens will change aperture and as such the DOF.
f-stops are a just certain number which is telling you the size of the aperture. That’s how f-stop is related to aperture (and DOF).
If you increase f-stops, it will increase DOF, the area in focus (and decrease the aperture). On the other hand, decreasing the f-stop it will decrease DOF (and increase the aperture).
The red cone in the figure is an angular representation of the resolution of the system. Versus the dotted lines, which indicate the aperture coverage. Where the lines of the two cones intersect defines the total range of the depth of field.
This image explains why the longer the depth of field, the greater the range of clarity.
They propose an end-to-end multimodality-conditioned human video generation framework named OmniHuman, which can generate human videos based on a single human image and motion signals (e.g., audio only, video only, or a combination of audio and video). In OmniHuman, we introduce a multimodality motion conditioning mixed training strategy, allowing the model to benefit from data scaling up of mixed conditioning. This overcomes the issue that previous end-to-end approaches faced due to the scarcity of high-quality data. OmniHuman significantly outperforms existing methods, generating extremely realistic human videos based on weak signal inputs, especially audio. It supports image inputs of any aspect ratio, whether they are portraits, half-body, or full-body images, delivering more lifelike and high-quality results across various scenarios.
In color technology, color depth also known as bit depth, is either the number of bits used to indicate the color of a single pixel, OR the number of bits used for each color component of a single pixel.
When referring to a pixel, the concept can be defined as bits per pixel (bpp).
When referring to a color component, the concept can be defined as bits per component, bits per channel, bits per color (all three abbreviated bpc), and also bits per pixel component, bits per color channel or bits per sample (bps). Modern standards tend to use bits per component, but historical lower-depth systems used bits per pixel more often.
Color depth is only one aspect of color representation, expressing the precision with which the amount of each primary can be expressed; the other aspect is how broad a range of colors can be expressed (the gamut). The definition of both color precision and gamut is accomplished with a color encoding specification which assigns a digital code value to a location in a color space.
It lets you load any .cube LUT right in your browser, see the RGB curves, and use a split view on the Granger Test Image to compare the original vs. LUT-applied version in real time — perfect for spotting hue shifts, saturation changes, and contrast tweaks.
A panoramic canvas measuring 402 feet (122 meters) around and 45 feet (13.7 meters) high. It contained over 5,000 life-size portraits of war heroes, royalty and government officials from the Allies of World War I.
