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Code of #ACEZero is out. A new approach to SfM. Learn the 3D scene without image-to-image matching. Naturally avoids the explosion of complexity for many images. ACE0 shines if you have dense coverage of a scene. Posing 10k images and more? Sure!

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Eric Brachmann

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23,988 görüntüleme • 2 yıl önce •via X (Twitter)

Bilim & Teknoloji#ACEZero
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Struggling to manage your reference images in the Sub View palette? Use the new pop-up list to view all your reference images and select the one you want, without needing to tap through each image one by one. You can also rearrange the display order of images or remove them from the popup. Learn more here ↓ #clipstudio
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Struggling to manage your reference images in the Sub View palette? Use the new pop-up list to view all your reference images and select the one you want, without needing to tap through each image one by one. You can also rearrange the display order of images or remove them from the popup. Learn more here ↓ #clipstudio

CLIP STUDIO PAINT

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MASSIVE image viewing UX update! - The image viewer has been completely reworked! - You can now click to expand the image! - Switching images is now a lot less clunky! - You can now also use A+D to switch between images! (more below)
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MASSIVE image viewing UX update! - The image viewer has been completely reworked! - You can now click to expand the image! - Switching images is now a lot less clunky! - You can now also use A+D to switch between images! (more below)

Itaku

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Create a 3D model from a single image, set of images or a text prompt in < 1 minute 😮‍💨 This new AI paper called CAT3D shows us that it’ll keep getting easier to produce 3D models from 2D images — whether it’s a sparser real world 3D scan (a few photos instead of hundreds) or your favorite 2D image generator like Midjourney (just an image). How does this magic work? “This architecture is similar to video diffusion models, but with camera pose embeddings for each image instead of time embeddings. The generated views are passed into a robust 3D reconstruction pipeline to create the 3D representation (Zip-NeRF or 3DGS)”
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Create a 3D model from a single image, set of images or a text prompt in < 1 minute 😮‍💨 This new AI paper called CAT3D shows us that it’ll keep getting easier to produce 3D models from 2D images — whether it’s a sparser real world 3D scan (a few photos instead of hundreds) or your favorite 2D image generator like Midjourney (just an image). How does this magic work? “This architecture is similar to video diffusion models, but with camera pose embeddings for each image instead of time embeddings. The generated views are passed into a robust 3D reconstruction pipeline to create the 3D representation (Zip-NeRF or 3DGS)”

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CSM 3D AI: New image-to-3D AI program AI is reshaping the future of 3D design. This amazing tool transforms still images into 3D models, and the best part? You can animate them effortlessly using AI. Link in comment!
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CSM 3D AI: New image-to-3D AI program AI is reshaping the future of 3D design. This amazing tool transforms still images into 3D models, and the best part? You can animate them effortlessly using AI. Link in comment!

el.cine

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Ben Cullen, a prominent activist in Oxfordshire’s Raise the Colours movement and a friend of Ryan Bridge, is due to appear in court today accused of creating indecent images of children. He is alleged to have created 22 Category A indecent images of children, including one moving image. He is also accused of creating around 56 indecent images of children using AI.
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Ben Cullen, a prominent activist in Oxfordshire’s Raise the Colours movement and a friend of Ryan Bridge, is due to appear in court today accused of creating indecent images of children. He is alleged to have created 22 Category A indecent images of children, including one moving image. He is also accused of creating around 56 indecent images of children using AI.

Mukhtar

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Here's how you can isolate individual images from this ChatGPT Image 2.0 prompt. 1. Ask ChatGPT to label each individual frame with a number. 2. Then with the new image, ask again to isolate the specific scene. Video tutorial:
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Here's how you can isolate individual images from this ChatGPT Image 2.0 prompt. 1. Ask ChatGPT to label each individual frame with a number. 2. Then with the new image, ask again to isolate the specific scene. Video tutorial:

Jerrod Lew

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A team of researchers has introduced DragGAN, an innovative technique for manipulating generated images by dragging points of the image. Learn more: #draggan #gan #gans #research #neuralnetwork #neuralnetworks #generativeadversarialnetwork
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A team of researchers has introduced DragGAN, an innovative technique for manipulating generated images by dragging points of the image. Learn more: #draggan #gan #gans #research #neuralnetwork #neuralnetworks #generativeadversarialnetwork

80 LEVEL

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These images are edited using doodles We generated the image, added a couple of strokes. Then used Seedream 5.0 Pro to change the output Prompts and images in thread
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These images are edited using doodles We generated the image, added a couple of strokes. Then used Seedream 5.0 Pro to change the output Prompts and images in thread

Magnific

20,608 görüntüleme • 26 gün önce

I work at Adobe and we recently released this edit text in images in Firefly Boards 🤯 You can select text of different complexity that is part of your image and edit it with AI. Best to use similar length of font. Have you already tried?
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I work at Adobe and we recently released this edit text in images in Firefly Boards 🤯 You can select text of different complexity that is part of your image and edit it with AI. Best to use similar length of font. Have you already tried?

Kris Kashtanova

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This feature is what I am proud of the most! I have built an algorithm that searches images based on a color and the algorithm will sort images based on how close the color is to the color selected and how dominant that color is in the image.
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This feature is what I am proud of the most! I have built an algorithm that searches images based on a color and the algorithm will sort images based on how close the color is to the color selected and how dominant that color is in the image.

Film Stills - SceneStill.com

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Meta releases VGGSfM Visual Geometry Grounded Deep Structure From Motion Structure-from-motion (SfM) is a long-standing problem in the computer vision community, which aims to reconstruct the camera poses and 3D structure of a scene from a set of unconstrained 2D images. Classical frameworks solve this problem in an incremental manner by detecting and matching keypoints, registering images, triangulating 3D points, and conducting bundle adjustment. Recent research efforts have predominantly revolved around harnessing the power of deep learning techniques to enhance specific elements (e.g., keypoint matching), but are still based on the original, non-differentiable pipeline. Instead, we propose a new deep SfM pipeline VGGSfM, where each component is fully differentiable and thus can be trained in an end-to-end manner. To this end, we introduce new mechanisms and simplifications. First, we build on recent advances in deep 2D point tracking to extract reliable pixel-accurate tracks, which eliminates the need for chaining pairwise matches. Furthermore, we recover all cameras simultaneously based on the image and track features instead of gradually registering cameras. Finally, we optimise the cameras and triangulate 3D points via a differentiable bundle adjustment layer. We attain state-of-the-art performance on three popular datasets, CO3D, IMC Phototourism, and ETH3D.
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Meta releases VGGSfM Visual Geometry Grounded Deep Structure From Motion Structure-from-motion (SfM) is a long-standing problem in the computer vision community, which aims to reconstruct the camera poses and 3D structure of a scene from a set of unconstrained 2D images. Classical frameworks solve this problem in an incremental manner by detecting and matching keypoints, registering images, triangulating 3D points, and conducting bundle adjustment. Recent research efforts have predominantly revolved around harnessing the power of deep learning techniques to enhance specific elements (e.g., keypoint matching), but are still based on the original, non-differentiable pipeline. Instead, we propose a new deep SfM pipeline VGGSfM, where each component is fully differentiable and thus can be trained in an end-to-end manner. To this end, we introduce new mechanisms and simplifications. First, we build on recent advances in deep 2D point tracking to extract reliable pixel-accurate tracks, which eliminates the need for chaining pairwise matches. Furthermore, we recover all cameras simultaneously based on the image and track features instead of gradually registering cameras. Finally, we optimise the cameras and triangulate 3D points via a differentiable bundle adjustment layer. We attain state-of-the-art performance on three popular datasets, CO3D, IMC Phototourism, and ETH3D.

AK

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MiniMax H3 Instead of sharing the prompts for each of these videos, I thought it would be more useful to share how I created that prompts. All of the videos were generated with text-to-video. First, find an image with the kind of scene, composition and mood you want to recreate. I used a few YouTube playlist thumbnails as references but Pinterest is also a great place to find inspiration. You can even use your own old or nostalgic photographs. Then upload the image to ChatGPT and ask it to describe the scene. The description it gives you can essentially become your text-to-video prompt. From there, you can generate completely new scenes with a similar composition, atmosphere and cinematic language. You can of course use the reference image directly with image-to-video or as a first frame. But if the original image isn't yours, I prefer using it only as visual inspiration and recreating the scene through text-to-video. This is the prompt I use with ChatGPT: "Describe the scene in this image in English, focusing primarily on what is happening, the characters, their actions and body language, the setting and the overall atmosphere. Also briefly describe the composition, framing, camera angle, approximate lens choice, lighting, color palette and cinematic aesthetic. Keep it concise and scene-focused rather than overly technical."
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MiniMax H3 Instead of sharing the prompts for each of these videos, I thought it would be more useful to share how I created that prompts. All of the videos were generated with text-to-video. First, find an image with the kind of scene, composition and mood you want to recreate. I used a few YouTube playlist thumbnails as references but Pinterest is also a great place to find inspiration. You can even use your own old or nostalgic photographs. Then upload the image to ChatGPT and ask it to describe the scene. The description it gives you can essentially become your text-to-video prompt. From there, you can generate completely new scenes with a similar composition, atmosphere and cinematic language. You can of course use the reference image directly with image-to-video or as a first frame. But if the original image isn't yours, I prefer using it only as visual inspiration and recreating the scene through text-to-video. This is the prompt I use with ChatGPT: "Describe the scene in this image in English, focusing primarily on what is happening, the characters, their actions and body language, the setting and the overall atmosphere. Also briefly describe the composition, framing, camera angle, approximate lens choice, lighting, color palette and cinematic aesthetic. Keep it concise and scene-focused rather than overly technical."

Kōda

45,208 görüntüleme • 3 gün önce

The new GPT 4.o image generator is insane. We're creating a ton of product images for 7-8 figure DTC brands. I just created a guide on how you can do the same for: - Static Ads - Email Templates - Product images Reply AI and I'll send it over
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The new GPT 4.o image generator is insane. We're creating a ton of product images for 7-8 figure DTC brands. I just created a guide on how you can do the same for: - Static Ads - Email Templates - Product images Reply AI and I'll send it over

Daan Kroon | Email/SMS + CRO

36,830 görüntüleme • 1 yıl önce

Excited to release a research preview of Feature Lab, a playground where you can sculpt images by editing their features. It uses a SAE to extract the sparse features from an image embeddings model, and allows us to finely sculpt and tweak images.
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Excited to release a research preview of Feature Lab, a playground where you can sculpt images by editing their features. It uses a SAE to extract the sparse features from an image embeddings model, and allows us to finely sculpt and tweak images.

Gytis Daujotas

73,833 görüntüleme • 2 yıl önce

Adobe just released edit text in images in Firefly Boards 🤯 You can select text of different complexity that is part of your image and edit it with AI. Best to use similar length of font. Isn't this absolutely wild?
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Adobe just released edit text in images in Firefly Boards 🤯 You can select text of different complexity that is part of your image and edit it with AI. Best to use similar length of font. Isn't this absolutely wild?

Kris Kashtanova

63,266 görüntüleme • 11 ay önce

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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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.

Michael Black

22,182 görüntüleme • 8 ay önce

I threw a couple of Mac Baconai's images in the new WorldLabs model. The scene completion is pretty darn good.
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I threw a couple of Mac Baconai's images in the new WorldLabs model. The scene completion is pretty darn good.

martin_casado

21,106 görüntüleme • 1 yıl önce

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 ⬇️

Matthew

16,430 görüntüleme • 5 ay önce