Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Excited to share our latest work using synthetic image generation for common #DBS targeting sequences! From a single MP2RAGE acquisition, we can create T1, FGATIR, EDGE, GM nulled, etc. & also optimize image contrast. #neurotwitter #NeuroRad

Erik Middlebrooks, MD

8,481 subscribers

19,642 views • 3 years ago •via X (Twitter)

#DBS #neurotwitter #NeuroRad

Anya Rossi• Live Now

Private livecam show

7 Comments

Erik Middlebrooks, MD3 years ago

This has revolutionized our workflow with shorter scan times for equal or better image quality. Also beneficial for #7T with ability to adjust contrast post-acquisition to account for B1+ variations. @SeizureSurgeon @VishalPatel_MD @ElenaGreco_11

Andreas Horn3 years ago

Amazing!

A. K3 years ago

This is pretty dope Dr. Middlerooks!

Danny JJ Wang3 years ago

Congrats Erik ingenious idea! Is the software available?

Erik Middlebrooks, MD3 years ago

Sure! Will email you

Bryan Neth, MD, PhD3 years ago

This is great! @TerryBurnsLab

STLPNTING3 years ago

I really wish my doctors would refer me to there I have a CSF leak and I just keep getting pushed off as if I'm nothing. I'm 32 and have a 4 and 5 yr old and we are expecting December 3rd our 3rd child. If anyone can help get me into Mayo it would be a blessing.

Related Videos

Generate persistent 3D worlds from a single image, bigger and better than ever! We’re excited to share our latest results and invite you to try out our world generation model in a limited beta preview.

Generate persistent 3D worlds from a single image, bigger and better than ever! We’re excited to share our latest results and invite you to try out our world generation model in a limited beta preview.

World Labs

835,786 views • 1 year ago

Early R&D using our SP-6M dataset. Exploring image-to-3D reconstruction from single images, including heavily modified inputs (lighting, hair, etc). Still a work in progress.

Early R&D using our SP-6M dataset. Exploring image-to-3D reconstruction from single images, including heavily modified inputs (lighting, hair, etc). Still a work in progress.

3D Scanstore

53,370 views • 5 months ago

I'm excited to share our new work, VistaDream, which generates a 3D Gaussian field from a single-view image. The codes have already been released. Project page: (with interactive demos) Code: Paper:

I'm excited to share our new work, VistaDream, which generates a 3D Gaussian field from a single-view image. The codes have already been released. Project page: (with interactive demos) Code: Paper:

Yuan Liu

79,610 views • 1 year ago

We just released HaMeR, our latest work for Hand Mesh Recovery! We reconstruct hands in 3D from a single image with big improvements in accuracy and robustness. Code and models are available. Also colab and Hugging Face demo!

We just released HaMeR, our latest work for Hand Mesh Recovery! We reconstruct hands in 3D from a single image with big improvements in accuracy and robustness. Code and models are available. Also colab and Hugging Face demo!

Georgios Pavlakos

38,336 views • 2 years ago

📢 IntrinsiX: High-Quality PBR Generation using Image Priors 📢 From text input, we generate renderable PBR maps! Next to editable image generation, our predictions can be distilled into room-scale scenes using SDS for large-scale PBR texture generation. We first train separate LoRA modules for the intrinsic properties of albedo, rough/metal, normal. Then, we introduce cross-intrinsic attention using a rerendering loss with importance-weighted light sampling to enable coherent PBR generation. Our method outperforms text -> image -> PBR methods both in generalization and quality, since directly generating PBR maps does not suffer from the inherent ambiguity of intrinsic image decomposition. In addition, our design choice facilitates SDS-based PBR texture distillation. 🌍 🎥 Great work by Peter Kocsis, Lukas Höllein

📢 IntrinsiX: High-Quality PBR Generation using Image Priors 📢 From text input, we generate renderable PBR maps! Next to editable image generation, our predictions can be distilled into room-scale scenes using SDS for large-scale PBR texture generation. We first train separate LoRA modules for the intrinsic properties of albedo, rough/metal, normal. Then, we introduce cross-intrinsic attention using a rerendering loss with importance-weighted light sampling to enable coherent PBR generation. Our method outperforms text -> image -> PBR methods both in generalization and quality, since directly generating PBR maps does not suffer from the inherent ambiguity of intrinsic image decomposition. In addition, our design choice facilitates SDS-based PBR texture distillation. 🌍 🎥 Great work by Peter Kocsis, Lukas Höllein

Matthias Niessner

21,921 views • 1 year ago

We also present another paper at @SIGGRAPH 2023 on neural implicit 3D Morphable Models that can be used to create a dynamic 3D avatar from a single in-the-wild image. (Lead author Connor Lin).

Koki Nagano

12,776 views • 3 years ago

Introducing SDXL Turbo: A real-time text-to-image generation model. SDXL Turbo achieves state-of-the-art performance with a new distillation technology, enabling single-step image generation with unprecedented quality, reducing the required step count from 50 to just one. The code, research paper, and weights for non-commercial use are now available on our website. You can test SDXL Turbo on Stability AI’s image editing platform Clipdrop, with a beta demonstration of the real-time text-to-image generation capabilities. Learn more:

Introducing SDXL Turbo: A real-time text-to-image generation model. SDXL Turbo achieves state-of-the-art performance with a new distillation technology, enabling single-step image generation with unprecedented quality, reducing the required step count from 50 to just one. The code, research paper, and weights for non-commercial use are now available on our website. You can test SDXL Turbo on Stability AI’s image editing platform Clipdrop, with a beta demonstration of the real-time text-to-image generation capabilities. Learn more:

Stability AI

976,940 views • 2 years ago

#ICCV2025 🤩3D world generation is cool, but it is cooler to play with the worlds using 3D actions 👆💨, and see what happens! — Introducing *WonderPlay*: Now you can create dynamic 3D scenes that respond to your 3D actions from a single image! Web: 🧵1/7

#ICCV2025 🤩3D world generation is cool, but it is cooler to play with the worlds using 3D actions 👆💨, and see what happens! — Introducing WonderPlay: Now you can create dynamic 3D scenes that respond to your 3D actions from a single image! Web: 🧵1/7

Hong-Xing (Koven) Yu

57,960 views • 1 year ago

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

AK

305,729 views • 3 years ago

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 views • 2 years ago

"The “ChatGPT moment” for physical AI is nearly here. 👏 Cosmos open world foundation models understand reality to create and reason about rich synthetic worlds—so self-driving cars and robots can learn from endlessly varied, physics-aware scenarios. From a single image, 3D scene, or simulator trace, Cosmos generates realistic video, brings edge cases to life, and helps autonomous systems train for the long tail. Watch the #CES2026 demo now 🎥

"The “ChatGPT moment” for physical AI is nearly here. 👏 Cosmos open world foundation models understand reality to create and reason about rich synthetic worlds—so self-driving cars and robots can learn from endlessly varied, physics-aware scenarios. From a single image, 3D scene, or simulator trace, Cosmos generates realistic video, brings edge cases to life, and helps autonomous systems train for the long tail. Watch the #CES2026 demo now 🎥

NVIDIA AI

49,809 views • 8 months ago

[#CollovResearch] Excited to share our latest collaborative research with Yale University Berkeley AI Research(AI Lab) and NUS on DEdit( a cutting-edge method for precise and disentangled image editing ranked as the No.2 project, surpassing #flux1, incredible achievement for us! This milestone showcases our breakthrough in image editing technology and reflects the growing anticipation and recognition from the communityThis innovative approach focuses on making edits without altering or losing information from the original image, using a learnable prompt editing component for diffusion models. At #CollovLabs, we’ve extended #DEdit to make it accessible to users with diverse editing needs through an easy-to-use API. This enhanced pipeline improves segmentation and provides step-by-step guidance, allowing for seamless and faithful image editing via natural language input. Hugging Face : Full Paper: Paper authors: AosongFeng, Weikang Qiu, Rex Ying, Jinbin Bai,Xiao Zhang KC Zhou Zhen Dong Leandros Tassiulas

[#CollovResearch] Excited to share our latest collaborative research with Yale University Berkeley AI Research(AI Lab) and NUS on DEdit( a cutting-edge method for precise and disentangled image editing ranked as the No.2 project, surpassing #flux1, incredible achievement for us! This milestone showcases our breakthrough in image editing technology and reflects the growing anticipation and recognition from the communityThis innovative approach focuses on making edits without altering or losing information from the original image, using a learnable prompt editing component for diffusion models. At #CollovLabs, we’ve extended #DEdit to make it accessible to users with diverse editing needs through an easy-to-use API. This enhanced pipeline improves segmentation and provides step-by-step guidance, allowing for seamless and faithful image editing via natural language input. Hugging Face : Full Paper: Paper authors: AosongFeng, Weikang Qiu, Rex Ying, Jinbin Bai,Xiao Zhang KC Zhou Zhen Dong Leandros Tassiulas

NewEyes AI (by Collov Labs)

10,607 views • 1 year ago

There’s no better gift we can leave the next generation than an independent country that’s theirs to create in their own image. No one can govern our country better than the people of Scotland. Let’s not just believe in a better future for Scotland. Let’s make it happen.

There’s no better gift we can leave the next generation than an independent country that’s theirs to create in their own image. No one can govern our country better than the people of Scotland. Let’s not just believe in a better future for Scotland. Let’s make it happen.

Humza Yousaf

376,686 views • 2 years ago

📢WorldAgents: 3D worlds only from 2D image models - without any training! We propose an agentic approach with a Director (VLM) to plan the scene, a Generator (Flux or NanoBanana) for new views, and a Verifier (VLM) for selection / 3D consistency. -> High-fidelity 3D worlds from a single text prompt. What's remarkable: our agents find consistent views from 2D image models to obtain 3D-consistent worlds; this shows that image models contain world priors - agents just need to find them! Great work by Ziya Erkoç Angela Dai

📢WorldAgents: 3D worlds only from 2D image models - without any training! We propose an agentic approach with a Director (VLM) to plan the scene, a Generator (Flux or NanoBanana) for new views, and a Verifier (VLM) for selection / 3D consistency. -> High-fidelity 3D worlds from a single text prompt. What's remarkable: our agents find consistent views from 2D image models to obtain 3D-consistent worlds; this shows that image models contain world priors - agents just need to find them! Great work by Ziya Erkoç Angela Dai

Matthias Niessner

19,093 views • 6 months ago

Super excited for #ECCV'26 in Malmö! Our group will present a series of cutting-edge works, including two orals and a spotlight, around generative AI that cover 3D scene generation, photo-realistic avatars, agentic modeling, and much more! I'll also give several workshop talks about our recent research, including a few exciting updates what we're cooking at SpAItial AI :) 1. GaussianGPT: Towards Autoregressive 3D Gaussian Scene Generation *(oral)* 2. TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields *(oral)* 3. Face Anything: 4D Face Reconstruction from Any Image Sequence *(spotlight)* 4. Pix2NPHM: Learning to Regress NPHM Reconstructions From a Single Image 5. Seen2Scene: Completing Realistic 3D Scenes with Visibility-Guided Flow 6. DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing 7. WorldAgents: Can Foundation Image Models be Agents for 3D World Models? 8. World Reconstruction From Inconsistent Views 9. Head Avatars with Dynamic Explicit Hair Great work by Nicolas von Lützow Haoxuan Li Ziya Erkoç Umut Kocasarı Vanessa Sklyarova Lukas Höllein Quan Meng Jinxin Ai Simon Giebenhain and all amazing collaborators!

Super excited for #ECCV'26 in Malmö! Our group will present a series of cutting-edge works, including two orals and a spotlight, around generative AI that cover 3D scene generation, photo-realistic avatars, agentic modeling, and much more! I'll also give several workshop talks about our recent research, including a few exciting updates what we're cooking at SpAItial AI :) 1. GaussianGPT: Towards Autoregressive 3D Gaussian Scene Generation (oral) 2. TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields (oral) 3. Face Anything: 4D Face Reconstruction from Any Image Sequence (spotlight) 4. Pix2NPHM: Learning to Regress NPHM Reconstructions From a Single Image 5. Seen2Scene: Completing Realistic 3D Scenes with Visibility-Guided Flow 6. DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing 7. WorldAgents: Can Foundation Image Models be Agents for 3D World Models? 8. World Reconstruction From Inconsistent Views 9. Head Avatars with Dynamic Explicit Hair Great work by Nicolas von Lützow Haoxuan Li Ziya Erkoç Umut Kocasarı Vanessa Sklyarova Lukas Höllein Quan Meng Jinxin Ai Simon Giebenhain and all amazing collaborators!

Matthias Niessner

14,897 views • 20 days ago

📢📢 𝐏𝐞𝐫𝐜𝐇𝐞𝐚𝐝: 𝐏𝐞𝐫𝐜𝐞𝐩𝐭𝐮𝐚𝐥 𝐇𝐞𝐚𝐝 𝐌𝐨𝐝𝐞𝐥 𝐟𝐨𝐫 𝐒𝐢𝐧𝐠𝐥𝐞-𝐈𝐦𝐚𝐠𝐞 𝟑𝐃 𝐇𝐞𝐚𝐝 𝐑𝐞𝐜𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 & 𝐄𝐝𝐢𝐭𝐢𝐧𝐠📢📢 PercHead reconstructs realistic 3D heads from a single image and enables disentangled 3D editing via geometric controls and style inputs from images or text. At its core is a generalized 3D head decoder trained with perceptual supervision from DINOv2 and SAM 2.1. We find that our new perceptual loss formulation improves reconstruction fidelity compared to commonly-used methods such as LPIPS. Our trained reconstruction model is able to generate 3D-consistent heads from a single input image. Even with challenging side-view inputs, the model robustly infers missing regions for a coherent, high-fidelity output. In addition, our architecture seamlessly adapts to downstream tasks: by swapping the encoder, we can transform the model into a disentangled 3D editing pipeline. In this scenario, we can control geometry through - potentially hand-drawn - segmentation maps, and condition style via image or text prompt. We also provide an interactive GUI to enable the exploration of our editing pipeline. 🌍 📽️ Great work by Antonio Oroz and Tobias Kirschstein

📢📢 𝐏𝐞𝐫𝐜𝐇𝐞𝐚𝐝: 𝐏𝐞𝐫𝐜𝐞𝐩𝐭𝐮𝐚𝐥 𝐇𝐞𝐚𝐝 𝐌𝐨𝐝𝐞𝐥 𝐟𝐨𝐫 𝐒𝐢𝐧𝐠𝐥𝐞-𝐈𝐦𝐚𝐠𝐞 𝟑𝐃 𝐇𝐞𝐚𝐝 𝐑𝐞𝐜𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 & 𝐄𝐝𝐢𝐭𝐢𝐧𝐠📢📢 PercHead reconstructs realistic 3D heads from a single image and enables disentangled 3D editing via geometric controls and style inputs from images or text. At its core is a generalized 3D head decoder trained with perceptual supervision from DINOv2 and SAM 2.1. We find that our new perceptual loss formulation improves reconstruction fidelity compared to commonly-used methods such as LPIPS. Our trained reconstruction model is able to generate 3D-consistent heads from a single input image. Even with challenging side-view inputs, the model robustly infers missing regions for a coherent, high-fidelity output. In addition, our architecture seamlessly adapts to downstream tasks: by swapping the encoder, we can transform the model into a disentangled 3D editing pipeline. In this scenario, we can control geometry through - potentially hand-drawn - segmentation maps, and condition style via image or text prompt. We also provide an interactive GUI to enable the exploration of our editing pipeline. 🌍 📽️ Great work by Antonio Oroz and Tobias Kirschstein

Matthias Niessner

18,903 views • 10 months ago

Mixboard just got a major tune up! Today, we’re introducing a bundle of updates (with a ribbon on top 🎁) to help you keep exploring your ideas: - Nano Banana Pro: Create presentations with the content found directly on your boards using our latest image generation model - New File Types: Now also supporting PDF, HEIC, & TIFF - Multi-Board Projects: Organize multiple boards within one cohesive project - & more! Try it now ->

Mixboard just got a major tune up! Today, we’re introducing a bundle of updates (with a ribbon on top 🎁) to help you keep exploring your ideas: - Nano Banana Pro: Create presentations with the content found directly on your boards using our latest image generation model - New File Types: Now also supporting PDF, HEIC, & TIFF - Multi-Board Projects: Organize multiple boards within one cohesive project - & more! Try it now ->

Google Labs

455,557 views • 9 months ago