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.

cursor tracking on 3D CSS surfaces 👨‍🍳 inverse projection mappin' to calculate proximity in 3D space and apply an eased 0-1 value 🤙 the key: measuring the 4 corners, check the reveal 👇 (ideal for 2D canvas w/ 3D transforms)

jhey ʕ•ᴥ•ʔ

146,562 subscribers

124,148 views • 1 year ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

We built a 3D node just for Astra. Generate 3D faster in FLORA, iterate between 2D & 3D directly on the canvas, and export directly to Blender.

We built a 3D node just for Astra. Generate 3D faster in FLORA, iterate between 2D & 3D directly on the canvas, and export directly to Blender.

weber

12,637 views • 20 days ago

The @Framer Agent can now build complex 3D transforms for you - and you can fine-tune every bit on canvas 👇

The @Framer Agent can now build complex 3D transforms for you - and you can fine-tune every bit on canvas 👇

Rob Hope

14,365 views • 25 days ago

Semantically annotating 3D gaussian splats on the fly using gemini 3.1 + sparkjs 1. Load any 3D scene and hit scan 2. Get 2D detections from VLM 3. Cluster outputs & project into 3D world space 4. Save as a persistent 3D semantic layer Inspired by Alexander Chen's experiments with gemini visual intelligence. Just had to try to lift it from 2D to 3D!

Semantically annotating 3D gaussian splats on the fly using gemini 3.1 + sparkjs 1. Load any 3D scene and hit scan 2. Get 2D detections from VLM 3. Cluster outputs & project into 3D world space 4. Save as a persistent 3D semantic layer Inspired by Alexander Chen's experiments with gemini visual intelligence. Just had to try to lift it from 2D to 3D!

Bilawal Sidhu

56,910 views • 4 months ago

3D for Hana is here. The first canvas that combines 3D and 2D in real-time. Convert any shape to a 3D model in a non-destructive process. All systems (vectors, booleans, layouts, events, etc.) work together.

3D for Hana is here. The first canvas that combines 3D and 2D in real-time. Convert any shape to a 3D model in a non-destructive process. All systems (vectors, booleans, layouts, events, etc.) work together.

Spline

114,870 views • 9 months ago

Combining 2D and 3D in After Effects. In Foldspace, not every surface needs to be bent. Keep one flat for 2D artwork, and let the others exist in 3D space. This allows you to combine 2D and 3D objects in a single animation with no masks or tricks needed.

Combining 2D and 3D in After Effects. In Foldspace, not every surface needs to be bent. Keep one flat for 2D artwork, and let the others exist in 3D space. This allows you to combine 2D and 3D objects in a single animation with no masks or tricks needed.

Motion Princess

21,989 views • 6 months ago

LLaVA-3D A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness Recent advancements in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to effectively process and understand images and videos. However, the development of LMMs with 3D-awareness for 3D scene understanding has been hindered by the lack of large-scale 3D vision-language datasets and powerful 3D encoders. In this paper, we introduce a simple yet effective framework called LLaVA-3D. Leveraging the strong 2D understanding priors from LLaVA, our LLaVA-3D efficiently adapts LLaVA for 3D scene understanding without compromising 2D understanding capabilities. To achieve this, we employ a simple yet effective representation, 3D Patch, which connects 2D CLIP patch features with their corresponding positions in 3D space. By integrating the 3D Patches into 2D LMMs and employing joint 2D and 3D vision-language instruction tuning, we establish a unified architecture for both 2D image understanding and 3D scene understanding. Experimental results show that LLaVA-3D converges 3.5x faster than existing 3D LMMs when trained on 3D vision-language datasets. Moreover, LLaVA-3D not only achieves state-of-the-art performance across various 3D tasks but also maintains comparable 2D image understanding and vision-language conversation capabilities with LLaVA.

LLaVA-3D A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness Recent advancements in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to effectively process and understand images and videos. However, the development of LMMs with 3D-awareness for 3D scene understanding has been hindered by the lack of large-scale 3D vision-language datasets and powerful 3D encoders. In this paper, we introduce a simple yet effective framework called LLaVA-3D. Leveraging the strong 2D understanding priors from LLaVA, our LLaVA-3D efficiently adapts LLaVA for 3D scene understanding without compromising 2D understanding capabilities. To achieve this, we employ a simple yet effective representation, 3D Patch, which connects 2D CLIP patch features with their corresponding positions in 3D space. By integrating the 3D Patches into 2D LMMs and employing joint 2D and 3D vision-language instruction tuning, we establish a unified architecture for both 2D image understanding and 3D scene understanding. Experimental results show that LLaVA-3D converges 3.5x faster than existing 3D LMMs when trained on 3D vision-language datasets. Moreover, LLaVA-3D not only achieves state-of-the-art performance across various 3D tasks but also maintains comparable 2D image understanding and vision-language conversation capabilities with LLaVA.

AK

41,747 views • 2 years ago

Thank you Figma for building the best 3D software ever! Jokes aside, this is probably the first and only 3D engine built in Figma. It uses mathematical transformations to project 3D coordinates on a 2D plane (your screen) Check it here:

Thank you Figma for building the best 3D software ever! Jokes aside, this is probably the first and only 3D engine built in Figma. It uses mathematical transformations to project 3D coordinates on a 2D plane (your screen) Check it here:

DoubleGlitch 🇺🇦

106,218 views • 3 years ago

Always have an SVG goo filter ready to go in your UI toolkit 🎯 .container { filter: url(#goo); } One line of CSS on the container + the SVG filter 🤙 Throw a border on for "Fake 3D" 👀 SVG filter code below 👇

Always have an SVG goo filter ready to go in your UI toolkit 🎯 .container { filter: url(#goo); } One line of CSS on the container + the SVG filter 🤙 Throw a border on for "Fake 3D" 👀 SVG filter code below 👇

jhey ʕ•ᴥ•ʔ

189,799 views • 2 years ago

Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors paper page: Most 3D generation research focuses on up-projecting 2D foundation models into the 3D space, either by minimizing 2D Score Distillation Sampling (SDS) loss or fine-tuning on multi-view datasets. Without explicit 3D priors, these methods often lead to geometric anomalies and multi-view inconsistency. Recently, researchers have attempted to improve the genuineness of 3D objects by directly training on 3D datasets, albeit at the cost of low-quality texture generation due to the limited texture diversity in 3D datasets. To harness the advantages of both approaches, we propose Bidirectional Diffusion(BiDiff), a unified framework that incorporates both a 3D and a 2D diffusion process, to preserve both 3D fidelity and 2D texture richness, respectively. Moreover, as a simple combination may yield inconsistent generation results, we further bridge them with novel bidirectional guidance. In addition, our method can be used as an initialization of optimization-based models to further improve the quality of 3D model and efficiency of optimization, reducing the generation process from 3.4 hours to 20 minutes. Experimental results have shown that our model achieves high-quality, diverse, and scalable 3D generation.

Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors paper page: Most 3D generation research focuses on up-projecting 2D foundation models into the 3D space, either by minimizing 2D Score Distillation Sampling (SDS) loss or fine-tuning on multi-view datasets. Without explicit 3D priors, these methods often lead to geometric anomalies and multi-view inconsistency. Recently, researchers have attempted to improve the genuineness of 3D objects by directly training on 3D datasets, albeit at the cost of low-quality texture generation due to the limited texture diversity in 3D datasets. To harness the advantages of both approaches, we propose Bidirectional Diffusion(BiDiff), a unified framework that incorporates both a 3D and a 2D diffusion process, to preserve both 3D fidelity and 2D texture richness, respectively. Moreover, as a simple combination may yield inconsistent generation results, we further bridge them with novel bidirectional guidance. In addition, our method can be used as an initialization of optimization-based models to further improve the quality of 3D model and efficiency of optimization, reducing the generation process from 3.4 hours to 20 minutes. Experimental results have shown that our model achieves high-quality, diverse, and scalable 3D generation.

AK

38,853 views • 2 years ago

CSS card parallax with pointermove + custom props 🧑‍🍳 <div class=ring style="--i: 2"> .ring { translate: calc(var(--px) * (var(--i) * -2rem)) calc(var(--py) * (var(--i) * 2rem)); } trick: pointer mapping to rotate card in 3D + translate rings 2D for the illusion 🤙 check it 👇

CSS card parallax with pointermove + custom props 🧑‍🍳 <div class=ring style="--i: 2"> .ring { translate: calc(var(--px) * (var(--i) * -2rem)) calc(var(--py) * (var(--i) * 2rem)); } trick: pointer mapping to rotate card in 3D + translate rings 2D for the illusion 🤙 check it 👇

jhey ʕ•ᴥ•ʔ

306,875 views • 10 months ago

Check this out!🔥 Modified version of TripoSR (open source 2D to 3D tool). This version outputs USDZ directly for Apple Vision Pro.🥽 You can easily create 3D objects in AVP with one click & view them in your space! Modified repo in the thread 🧵👇

Check this out!🔥 Modified version of TripoSR (open source 2D to 3D tool). This version outputs USDZ directly for Apple Vision Pro.🥽 You can easily create 3D objects in AVP with one click & view them in your space! Modified repo in the thread 🧵👇

Blaine Brown

48,111 views • 2 years ago

Zero Hand Tracking is here! Working on the OS and sensor integration. Accesibility is key, that's why apps are HTML/CSS/JS 2D web apps render as panels in front of you. It's really easy to make this kind of app for Zero 3D apps, like below, run over WebXR, here using #threejs

Zero Hand Tracking is here! Working on the OS and sensor integration. Accesibility is key, that's why apps are HTML/CSS/JS 2D web apps render as panels in front of you. It's really easy to make this kind of app for Zero 3D apps, like below, run over WebXR, here using #threejs

Miroslav Kotalík

13,123 views • 1 year ago

🛸 IMPOSTORS HAVE ENTERED ANOTHER DIMENSION 🛸 🤯 3D traits re-imagined in 2D 📸 Use the Previewer to see YOURS 👽 New website ⏰Metadata will update on 10/31 with 2D<>3D Toggle 🧵👇 1/5

🛸 IMPOSTORS HAVE ENTERED ANOTHER DIMENSION 🛸 🤯 3D traits re-imagined in 2D 📸 Use the Previewer to see YOURS 👽 New website ⏰Metadata will update on 10/31 with 2D<>3D Toggle 🧵👇 1/5

Impostors

67,965 views • 2 years ago

3d visual positioning experiment -- look at the alignment between the 3d mesh and the live camera view. Truly feels magical -- like x-ray vision. Workflow: 1. Scanned a street in 15 mins w/ xgrids 2. Localized against that scan at night, in real-time, while sitting in a car The xgrids scanner (w/ rgb + lidar) is perfect to build maps for humans (3d gaussian splats). Then multiset ai makes it easy to build machine-readable maps for AR and robotics to figure out exactly where they are in 3d space with cm-level accuracy.

3d visual positioning experiment -- look at the alignment between the 3d mesh and the live camera view. Truly feels magical -- like x-ray vision. Workflow: 1. Scanned a street in 15 mins w/ xgrids 2. Localized against that scan at night, in real-time, while sitting in a car The xgrids scanner (w/ rgb + lidar) is perfect to build maps for humans (3d gaussian splats). Then multiset ai makes it easy to build machine-readable maps for AR and robotics to figure out exactly where they are in 3d space with cm-level accuracy.

Bilawal Sidhu

27,485 views • 10 months ago

Did you know that in Blender you can easily bend objects in edit mode by pressing Shift+W? ✨ 1. Select the geometry to bend from 2. Press Shift+S,2 to place the 3d cursor there 3. Select the geometry to bend 4. Press Shift+W to bend! Important: when you're pressing Shift+W, hover your mouse over the geo you're trying to bend, like shown in the video! Bonus: by placing the 3d cursor slightly off center and holding ALT (to disable clamping) while bending, you can easily create spirals! #b3d

Did you know that in Blender you can easily bend objects in edit mode by pressing Shift+W? ✨ 1. Select the geometry to bend from 2. Press Shift+S,2 to place the 3d cursor there 3. Select the geometry to bend 4. Press Shift+W to bend! Important: when you're pressing Shift+W, hover your mouse over the geo you're trying to bend, like shown in the video! Bonus: by placing the 3d cursor slightly off center and holding ALT (to disable clamping) while bending, you can easily create spirals! #b3d

passivestar

1,050,555 views • 3 years ago

📢 Intrinsic Image Fusion for Multi-View 3D Material Reconstruction 📢 We combine generative material priors with inverse path tracing: 1) define a parametric texture space 2) fuse monocular predictions across views into consistent textures 3) optimize low-dimensional parameters for physically-grounded reconstructions. The results are relightable PBR textures for 3D scenes: check out the result on a real-world 3D scan from the ScanNet++ dataset! 🌍 🎥 Great work by Peter Kocsis Lukas Höllein!

📢 Intrinsic Image Fusion for Multi-View 3D Material Reconstruction 📢 We combine generative material priors with inverse path tracing: 1) define a parametric texture space 2) fuse monocular predictions across views into consistent textures 3) optimize low-dimensional parameters for physically-grounded reconstructions. The results are relightable PBR textures for 3D scenes: check out the result on a real-world 3D scan from the ScanNet++ dataset! 🌍 🎥 Great work by Peter Kocsis Lukas Höllein!

Matthias Niessner

23,285 views • 9 months ago

"The Space Explorers", an old personal animated project by illustrator James Noellert, which he started in 2D almost 10 years ago and is now continuing to develop in 3D using Blender.

"The Space Explorers", an old personal animated project by illustrator James Noellert, which he started in 2D almost 10 years ago and is now continuing to develop in 3D using Blender.

Catsuka

20,229 views • 7 days ago

Volumetric, animated 3D noise shader block in Unity. Where each frame is 2D space, and the 3rd dimension is time. So that, measurement of change within a slice is 'space', and measurement of change across slices is 'time'. As a 3D block, you see the noise animate into the 3rd dimension, but when looking at only a single slice, it appears to evolve in place. Likewise, for 4D... If: this were 3D noise, then: the 4th dimension would be whatever processing/frames/states of 3D data.

Volumetric, animated 3D noise shader block in Unity. Where each frame is 2D space, and the 3rd dimension is time. So that, measurement of change within a slice is 'space', and measurement of change across slices is 'time'. As a 3D block, you see the noise animate into the 3rd dimension, but when looking at only a single slice, it appears to evolve in place. Likewise, for 4D... If: this were 3D noise, then: the 4th dimension would be whatever processing/frames/states of 3D data.

Mirza Beig

126,907 views • 9 months ago

📢 OneCanvas: 3D Scene Understanding via Panoramic Reprojection We extract features from video frames and reproject them into one occlusion-free view of the whole scene that a 2D VLM reads just like a normal image. We can center this view on any viewpoint, including an agent's own pose for situated reasoning. The same projection lets us create spatial training tasks with no human annotation, solvable only by reasoning over the 3D positions of real object features placed on an otherwise empty canvas. The result is a stock 2D VLM that reasons in 3D, setting a new state of the art across spatial benchmarks at far less compute. 🌐 ▶️ Great work by Bartłomiej Baranowski & Dave Zhenyu Chen

📢 OneCanvas: 3D Scene Understanding via Panoramic Reprojection We extract features from video frames and reproject them into one occlusion-free view of the whole scene that a 2D VLM reads just like a normal image. We can center this view on any viewpoint, including an agent's own pose for situated reasoning. The same projection lets us create spatial training tasks with no human annotation, solvable only by reasoning over the 3D positions of real object features placed on an otherwise empty canvas. The result is a stock 2D VLM that reasons in 3D, setting a new state of the art across spatial benchmarks at far less compute. 🌐 ▶️ Great work by Bartłomiej Baranowski & Dave Zhenyu Chen

Matthias Niessner

25,405 views • 3 months ago