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One tool. Total lighting control for images and videos Relight just launched on Freepik → Control direction, intensity, and color → Transfer lighting from any reference image → Apply expert presets and adjust from there A whole lighting studio in your browser

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Magnific

89,979 subscribers

62,152 просмотров • 6 месяцев назад •via X (Twitter)

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Designers should know this Lighting Hack 🤠 Control the lighting of any image. Change direction, color, intensity, even turn day into night. Comment “try” and I’ll send it ⤵️
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Designers should know this Lighting Hack 🤠 Control the lighting of any image. Change direction, color, intensity, even turn day into night. Comment “try” and I’ll send it ⤵️

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This Relight tool is insane 😱 You can completely change the lighting of any image in seconds. If you work with images, you need to try this. Comment “Relight” and I’ll send it to you ⬇️
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This Relight tool is insane 😱 You can completely change the lighting of any image in seconds. If you work with images, you need to try this. Comment “Relight” and I’ll send it to you ⬇️

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Depth video workflows have been getting a lot of attention lately, so I tested one myself. Combined with Seedance 2.0, it produced more natural motion-transfer results than using Kling Motion Control directly. Why use a depth video? 1. It removes the original character and scene details, reducing copyright and sensitive-content risks. 2. It preserves the original motion, timing, and spatial structure. This separates motion extraction from visual generation, allowing you to recreate the movement with better models and any reference character. We’ve also launched a free online tool that converts regular videos into depth videos—no local setup required: In the example below, we converted a dance video from Douyin into a depth video, then regenerated it with a reference character using Seedance 2.0. The original choreography and timing are preserved, while the lighting adapts naturally to the new character and scene.

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Video diffusion models have strong implicit representations of 3D shape, material, and lighting, but controlling them with language is cumbersome, and control is critical for artists and animators. GenLit connects these implicit representations with a continuous 5D control signal describing the direction and intensity of a point light source. This enables single-image near-field relighting of an image using a video diffusion model. We use a ControlNet-like approach and show that, with a small amount of synthetic data, GenLit generalizes to complex real-world images. Given a single image and the 5D lighting signal, GenLit creates a video of a moving light source that is inside the scene. It moves around and behind scene objects, producing effects such as shading, cast shadows, secularities, and interreflections with a realism that is hard to obtain with traditional inverse rendering methods. GenLit shows that it is possible to get continuous control over implicit physical processes within a video model. I think this is just the beginning and promises to make such models much more practical for creators. Shrisha Bharadwaj will present today at SIGGRAPH Asia Room: S423/S424, Level 4 @ 13:50 on 15 of Dec.

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