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NVidia DiffusionRenderer – Neural Inverse and Forward Rendering with Video Diffusion Models. How NVIDIA reimagined relighting
Read more: NVidia DiffusionRenderer – Neural Inverse and Forward Rendering with Video Diffusion Models. How NVIDIA reimagined relightinghttps://www.fxguide.com/quicktakes/diffusing-reality-how-nvidia-reimagined-relighting/
https://research.nvidia.com/labs/toronto-ai/DiffusionRenderer/
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Photography basics: Lumens vs Candelas (candle) vs Lux vs FootCandle vs Watts vs Irradiance vs Illuminance
Read more: Photography basics: Lumens vs Candelas (candle) vs Lux vs FootCandle vs Watts vs Irradiance vs Illuminancehttps://www.translatorscafe.com/unit-converter/en-US/illumination/1-11/
The power output of a light source is measured using the unit of watts W. This is a direct measure to calculate how much power the light is going to drain from your socket and it is not relatable to the light brightness itself.
The amount of energy emitted from it per second. That energy comes out in a form of photons which we can crudely represent with rays of light coming out of the source. The higher the power the more rays emitted from the source in a unit of time.
Not all energy emitted is visible to the human eye, so we often rely on photometric measurements, which takes in account the sensitivity of human eye to different wavelenghts
Details in the post
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Insta360-Research-Team DiT360 – High-Fidelity Panoramic Image Generation via Hybrid Training
Read more: Insta360-Research-Team DiT360 – High-Fidelity Panoramic Image Generation via Hybrid Traininghttps://github.com/Insta360-Research-Team/DiT360
DiT360 is a framework for high-quality panoramic image generation, leveraging both perspective and panoramic data in a hybrid training scheme. It adopts a two-level strategy—image-level cross-domain guidance and token-level hybrid supervision—to enhance perceptual realism and geometric fidelity.
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