Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Excited to share 🎨🖌️ 3D Paintbrush - a method for generating local stylized textures on meshes using text as input! Our method predicts a localization map & a highly detailed texture map which conforms to it (1/3)

Rana Hanocka

6,082 subscribers

49,037 görüntüleme • 2 yıl önce •via X (Twitter)

Bilim & Teknoloji

Anya Rossi• Live Now

Private livecam show

9 Yorum

Rana Hanocka profil fotoğrafı

Rana Hanocka2 yıl önce

3D Paintbrush produces textures that effectively adhere to the localizations. This enables seamlessly compositing local textures without any unwanted fringes! (2/3)

Rana Hanocka profil fotoğrafı

Rana Hanocka2 yıl önce

Key to our method is cascaded score distillation (CSD): which simultaneously distills scores at multiple resolutions. Standard SDS only uses the first low-res stage. Incorporating the super-res cascaded stage from our CSD increases the resolution and detail! (3/3)

Rana Hanocka profil fotoğrafı

Rana Hanocka2 yıl önce

3D Paintbrush was led by 3DL PhD student @DecaturDale 🚀. Other co-authors are: 3DL postdoc @ItaiLang and Snap Researcher @AbermanKfir (/4)

Keenan Crane profil fotoğrafı

Keenan Crane2 yıl önce

You’ve made Spot very happy. 🐮

Rana Hanocka profil fotoğrafı

Rana Hanocka2 yıl önce

Our whole lab is obsessed with spot! 🐄❤️ Thanks for making him! Bob also made an appearance in this paper 🐥🙂

Nitin Agarwal profil fotoğrafı

Nitin Agarwal2 yıl önce

Really nice work! I wonder how far we are from conditioned (text/image) geometric editing as well.

Nikhila Ravi profil fotoğrafı

Nikhila Ravi2 yıl önce

Wow!! So cool! 🤩

Daniel profil fotoğrafı

Daniel2 yıl önce

Awesome

Tariq Hussain profil fotoğrafı

Tariq Hussain2 yıl önce

wow amazing work

Benzer Videolar

Nvidia presents Edify 3D! The method can generate high-quality 3D assets from text descriptions. It uses a diffusion model to create detailed quad-mesh topologies and high-resolution textures in under 2 minutes.

Nvidia presents Edify 3D! The method can generate high-quality 3D assets from text descriptions. It uses a diffusion model to create detailed quad-mesh topologies and high-resolution textures in under 2 minutes.

Dreaming Tulpa 🥓👑

39,517 görüntüleme • 1 yıl önce

(1/4) Excited to share our #ICCV2023 paper Text2Room! We generate scene-scale textured 3D meshes from a given text prompt leveraging 2D text-to-image models such as StableDiffusion. Project: Code: Video:

(1/4) Excited to share our #ICCV2023 paper Text2Room! We generate scene-scale textured 3D meshes from a given text prompt leveraging 2D text-to-image models such as StableDiffusion. Project: Code: Video:

Matthias Niessner

74,905 görüntüleme • 3 yıl önce

Excited to share our work on Neural Assets: a new method for enabling 3D asset-level control in image diffusion models – scalable & without any 3D inductive biases. Neural Assets goes beyond text or pixel-based control & provides an interface inspired by 3D graphics tools. 🧵

Excited to share our work on Neural Assets: a new method for enabling 3D asset-level control in image diffusion models – scalable & without any 3D inductive biases. Neural Assets goes beyond text or pixel-based control & provides an interface inspired by 3D graphics tools. 🧵

Thomas Kipf

97,924 görüntüleme • 2 yıl önce

A new EEG microdisplay for #neurosurgery conforms to the brain and employs colorful light patterns to visualize brain activity in large animals, potentially providing a more reliable method for surgeons to map and avoid functional brain tissue. Science Translational Medicine

A new EEG microdisplay for #neurosurgery conforms to the brain and employs colorful light patterns to visualize brain activity in large animals, potentially providing a more reliable method for surgeons to map and avoid functional brain tissue. Science Translational Medicine

Science Magazine

68,526 görüntüleme • 2 yıl önce

Check out our fun work on robust view synthesis! 🤩 Using a casual video as input, our method jointly 1⃣estimates accurate camera poses and 2⃣reconstructs multiple local radiance fields for a large scene. Project🕸️:

Check out our fun work on robust view synthesis! 🤩 Using a casual video as input, our method jointly 1⃣estimates accurate camera poses and 2⃣reconstructs multiple local radiance fields for a large scene. Project🕸️:

Jia-Bin Huang

35,526 görüntüleme • 3 yıl önce

Thrilled to share our new work on Reconviagen✨! A key challenge in 3D creation is the alignment of generative 3D with observational input. Our method solves this by grounding the generative process in 3D reconstruction. Try it at: #Reconstruction #AIGC

Thrilled to share our new work on Reconviagen✨! A key challenge in 3D creation is the alignment of generative 3D with observational input. Our method solves this by grounding the generative process in 3D reconstruction. Try it at: #Reconstruction #AIGC

Chongjie Ye

11,624 görüntüleme • 11 ay önce

🖌️ Painting a flat grey model and not sure where to start? Kickstart your stylized texture painting in Toolbag with a baked AO map, block in values with overlays, then quickly block colors in using gradient remap layers 🎨 This lets you nail form first, and add color later.

🖌️ Painting a flat grey model and not sure where to start? Kickstart your stylized texture painting in Toolbag with a baked AO map, block in values with overlays, then quickly block colors in using gradient remap layers 🎨 This lets you nail form first, and add color later.

Marmoset

12,999 görüntüleme • 4 ay önce

I’m trying to re-texture my avatar in #EmoteLab it’s very cute! > Texture Cache > CoffeeBean > Custom Textures - Go back to EmoteLab and click Reload Textures. Method 2: Easier Way to Edit Textures Directly - Go to Settings → Open Character Folder. - Find the version you are using, then open your character folder. - Click Regions. - You will find the textures used by the character there. You can edit these textures directly as well. 💡Tip: I recommend editing the WeightTransfer folder first, then Click Unload Current Model and Load the model again to refresh the textures. Finally, You will get your own EmoteLab texture!!

B U N R I👾 @Live2D

22,335 görüntüleme • 5 ay önce

Some of you may recall a Reddit post from few years ago, which demonstrated the potential for custom 3D enviroments during battles. Many moons later, discord user Shy presented a general method to convert map assets into battle backgrounds....

Some of you may recall a Reddit post from few years ago, which demonstrated the potential for custom 3D enviroments during battles. Many moons later, discord user Shy presented a general method to convert map assets into battle backgrounds....

AphexCubed

185,244 görüntüleme • 4 ay önce

🚨 TEXT TO 3D WORKS! 🚨 Excited to share a video on our text-to-3D (at runtime) AI engine powering the AI game Geppetto’s Workshop ! #AIart #gamedev #indiedev #GenerativeAI

🚨 TEXT TO 3D WORKS! 🚨 Excited to share a video on our text-to-3D (at runtime) AI engine powering the AI game Geppetto’s Workshop ! #AIart #gamedev #indiedev #GenerativeAI

Pillars Films

299,068 görüntüleme • 3 yıl önce

Using ImGui's font atlas to render text into an RGBA texture, then mapping it to a simple quad for in-game 3D text. Super useful for debugging and prototyping.

Using ImGui's font atlas to render text into an RGBA texture, then mapping it to a simple quad for in-game 3D text. Super useful for debugging and prototyping.

Ioannis Koukourakis

12,443 görüntüleme • 6 ay önce

Some of the raw map animation I made for a recent conference Ito were presenting at. The Miami city model was really detailed and it was fun finding ways to simulate disruptions on lines. #3d #map #animation

Some of the raw map animation I made for a recent conference Ito were presenting at. The Miami city model was really detailed and it was fun finding ways to simulate disruptions on lines. #3d #map #animation

Craig Taylor

30,648 görüntüleme • 2 yıl önce

I'm excited to share our new work Diffusion as Shader (DaS), a versatile controllable video generation method for various tasks: object manipulation, camera control, mesh-to-video, and motion transfer. Project page: Github:

I'm excited to share our new work Diffusion as Shader (DaS), a versatile controllable video generation method for various tasks: object manipulation, camera control, mesh-to-video, and motion transfer. Project page: Github:

Yuan Liu

33,467 görüntüleme • 1 yıl önce

.Tkay had to pull out the "mom-cam" to prove to Methodz & Zoomaa he's just different Tkay on the series (ELIMS): Map 1 - 58/29 Map 2 - 16/2 Map 3 - N/A Map 4 - 44/24 HIM 😤

.Tkay had to pull out the "mom-cam" to prove to Methodz & Zoomaa he's just different Tkay on the series (ELIMS): Map 1 - 58/29 Map 2 - 16/2 Map 3 - N/A Map 4 - 44/24 HIM 😤

Torn

181,662 görüntüleme • 3 ay önce

Excited to share my final PhD project😀 We show how simple, yet elegant changes enable diffusion transformers to learn SOTA robotic policies on real robots. Our method improves performance by 20% across a wide range of highly dexterous tasks - like cutting sushi! 1/n

Excited to share my final PhD project😀 We show how simple, yet elegant changes enable diffusion transformers to learn SOTA robotic policies on real robots. Our method improves performance by 20% across a wide range of highly dexterous tasks - like cutting sushi! 1/n

Sudeep Dasari

20,536 görüntüleme • 1 yıl önce

A teacher recorded the moment he asked a Primary 3 student to calculate 10 + 1, but she struggled to solve it without using her own method 👀

A teacher recorded the moment he asked a Primary 3 student to calculate 10 + 1, but she struggled to solve it without using her own method 👀

TENIOLA

444,395 görüntüleme • 6 ay önce

Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation paper page: Recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text, with methods that can generate character animations from short prompts and specified durations. However, using a single text prompt as input lacks the fine-grained control needed by animators, such as composing multiple actions and defining precise durations for parts of the motion. To address this, we introduce the new problem of timeline control for text-driven motion synthesis, which provides an intuitive, yet fine-grained, input interface for users. Instead of a single prompt, users can specify a multi-track timeline of multiple prompts organized in temporal intervals that may overlap. This enables specifying the exact timings of each action and composing multiple actions in sequence or at overlapping intervals. To generate composite animations from a multi-track timeline, we propose a new test-time denoising method. This method can be integrated with any pre-trained motion diffusion model to synthesize realistic motions that accurately reflect the timeline. At every step of denoising, our method processes each timeline interval (text prompt) individually, subsequently aggregating the predictions with consideration for the specific body parts engaged in each action. Experimental comparisons and ablations validate that our method produces realistic motions that respect the semantics and timing of given text prompts.

Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation paper page: Recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text, with methods that can generate character animations from short prompts and specified durations. However, using a single text prompt as input lacks the fine-grained control needed by animators, such as composing multiple actions and defining precise durations for parts of the motion. To address this, we introduce the new problem of timeline control for text-driven motion synthesis, which provides an intuitive, yet fine-grained, input interface for users. Instead of a single prompt, users can specify a multi-track timeline of multiple prompts organized in temporal intervals that may overlap. This enables specifying the exact timings of each action and composing multiple actions in sequence or at overlapping intervals. To generate composite animations from a multi-track timeline, we propose a new test-time denoising method. This method can be integrated with any pre-trained motion diffusion model to synthesize realistic motions that accurately reflect the timeline. At every step of denoising, our method processes each timeline interval (text prompt) individually, subsequently aggregating the predictions with consideration for the specific body parts engaged in each action. Experimental comparisons and ablations validate that our method produces realistic motions that respect the semantics and timing of given text prompts.

AK

126,585 görüntüleme • 2 yıl önce

The tweet I’m quoting is the list method. In this tweet I’m going to show you the MAP method. Step 1. Load up a game, I’ve loaded Burnley for this example. Step 2. Refresh the page every now and again until you see the map show a section blue. Step 3. Zoom in, see if the dot has loaded in (if not refresh till it does, but try not to refresh a lot) Step 4. When the dot loads, click on and off till it shows prices Here’s a video example of me missing a Burnley one.

The tweet I’m quoting is the list method. In this tweet I’m going to show you the MAP method. Step 1. Load up a game, I’ve loaded Burnley for this example. Step 2. Refresh the page every now and again until you see the map show a section blue. Step 3. Zoom in, see if the dot has loaded in (if not refresh till it does, but try not to refresh a lot) Step 4. When the dot loads, click on and off till it shows prices Here’s a video example of me missing a Burnley one.

Ballot News

135,702 görüntüleme • 3 ay önce

(1/3) insitro is excited to share our paper “EmbedGEM: A Framework to Evaluate the Utility of Embeddings for Genetic Discovery,” focusing on assessing the use of ML-derived embeddings for genetic discovery using heritability & relevance to specific diseases.

(1/3) insitro is excited to share our paper “EmbedGEM: A Framework to Evaluate the Utility of Embeddings for Genetic Discovery,” focusing on assessing the use of ML-derived embeddings for genetic discovery using heritability & relevance to specific diseases.

insitro

18,421 görüntüleme • 2 yıl önce

🚨 Pixal3D is now live on fal! 🧊 High-fidelity 3D assets from a single image 🎯 Pixel-aligned back-projection for direct pixel-to-3D correspondence 🎨 Detailed geometry with PBR textures, ready for downstream pipelines

🚨 Pixal3D is now live on fal! 🧊 High-fidelity 3D assets from a single image 🎯 Pixel-aligned back-projection for direct pixel-to-3D correspondence 🎨 Detailed geometry with PBR textures, ready for downstream pipelines

fal

14,192 görüntüleme • 2 ay önce