Загрузка видео...

Не удалось загрузить видео

На главную

A single image. An entire 3D world. And every important object is editable. Hyper3D WorldGen turns a single scene image into a structured 3D environment with: - Independent assets - Spatial relationships - Physical properties - The freedom to move, replace, refine, and interact with them This is scene-level...

this_is_tasnim's profile picture

Maliha Tasnim

8,609 subscribers

52,997 просмотров • 29 дней назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Imagine transforming a single image into a fully editable 3D world! With Hyper3D WorldGen, you can manipulate individual objects, making them movable and interactive. Ready to explore how this groundbreaking technology works? 🧵👇
0:11

Imagine transforming a single image into a fully editable 3D world! With Hyper3D WorldGen, you can manipulate individual objects, making them movable and interactive. Ready to explore how this groundbreaking technology works? 🧵👇

Auralix

44,255 просмотров • 20 дней назад

Hyper3D just launched WorldGen. Give it one image and it generates a complete, structured 3D scene, not one flattened model, but separate assets while preserving their spatial relationships. Here’s the generated scene, followed by AI Render bringing the environment to life:
0:19

Hyper3D just launched WorldGen. Give it one image and it generates a complete, structured 3D scene, not one flattened model, but separate assets while preserving their spatial relationships. Here’s the generated scene, followed by AI Render bringing the environment to life:

Alex Prompter

31,633 просмотров • 1 месяц назад

Here’s how to create this cool 3D scene from a single image! 📸 Midjourney (isometric image generation) Trellis (Image to 3D Gaussian Splat) Browser Lab (3D Editor Splat Import)
0:13

Here’s how to create this cool 3D scene from a single image! 📸 Midjourney (isometric image generation) Trellis (Image to 3D Gaussian Splat) Browser Lab (3D Editor Splat Import)

Ian Curtis

117,983 просмотров • 1 год назад

It’s crazy how a single image can turn into an immersive experience with World Labs. - 3D environment - Animated character + assets - Cursor / Spark 2.0 / three.js
0:18

It’s crazy how a single image can turn into an immersive experience with World Labs. - 3D environment - Animated character + assets - Cursor / Spark 2.0 / three.js

Brittani Natali

11,723 просмотров • 4 месяцев назад

The new Krea 3D feature is insanely good. You can turn an image into a 3D model to move & rotate in space - and it guides real-time scene generation. Watch me move this couch around a living room ⬇️
0:18

The new Krea 3D feature is insanely good. You can turn an image into a 3D model to move & rotate in space - and it guides real-time scene generation. Watch me move this couch around a living room ⬇️

Justine Moore

32,737 просмотров • 1 год назад

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.
0:12

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

MrNeRF

52,849 просмотров • 1 год назад

wait… this might be the coolest thing we’ve shipped all year. you upload ONE image and WorldGen doesn’t just give you a pretty 3D copy. it reads the scene — the objects, their positions, scale, orientation, and how they relate to each other. then it turns the key objects into separate 3D assets you can actually work with.
0:14

wait… this might be the coolest thing we’ve shipped all year. you upload ONE image and WorldGen doesn’t just give you a pretty 3D copy. it reads the scene — the objects, their positions, scale, orientation, and how they relate to each other. then it turns the key objects into separate 3D assets you can actually work with.

Parul Gautam

57,531 просмотров • 1 месяц назад

This AI 3D tool is on another level. A dragon head and a dungeon NPC. Both from a single image. Here is how:
0:11

This AI 3D tool is on another level. A dragon head and a dungeon NPC. Both from a single image. Here is how:

AI Highlight

141,349 просмотров • 3 месяцев назад

Static 3D generation isn't enough. We need assets ready for animation. Our new #SIGGRAPH work, AniGen, takes a single image and generates the 3D shape, skeleton, and skinning weights all at once. Code is fully open-sourced! Kudos to Yihua and VAST AI Research 🧵(1/4)
0:15

Static 3D generation isn't enough. We need assets ready for animation. Our new #SIGGRAPH work, AniGen, takes a single image and generates the 3D shape, skeleton, and skinning weights all at once. Code is fully open-sourced! Kudos to Yihua and VAST AI Research 🧵(1/4)

Yanpei Cao

145,743 просмотров • 5 месяцев назад

Garment3DGen 3D Garment Stylization and Texture Generation We introduce Garment3DGen a new method to synthesize 3D garment assets from a base mesh given a single input image as guidance. Our proposed approach allows users to generate 3D textured clothes based on both
0:16

Garment3DGen 3D Garment Stylization and Texture Generation We introduce Garment3DGen a new method to synthesize 3D garment assets from a base mesh given a single input image as guidance. Our proposed approach allows users to generate 3D textured clothes based on both

AK

53,158 просмотров • 2 лет назад

3d world abilities of AI models keep getting better, this is Kling AI 1.5 It can move around a space in 3d and you can steer its camera movements All based on a single input image
0:10

3d world abilities of AI models keep getting better, this is Kling AI 1.5 It can move around a space in 3d and you can steer its camera movements All based on a single input image

@levelsio

112,222 просмотров • 2 лет назад

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.
0:18

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.

MrNeRF

17,062 просмотров • 1 год назад

Huawei's new 3D lock screen wallpaper is actually pretty interesting. Unlike iPhone's Spatial Photo, which creates a fixed stereoscopic depth effect from a photo, this lets you scan an object 360°, generate a full 3D Gaussian Splatting asset, and use that as your lock screen. Tilt the phone and you're moving through a reconstructed 3D scene, not a fixed stereo image. The part worth paying attention to: 3D capture moving out of creative pipelines and into everyday phone personalization. That's a different kind of adoption curve.
0:16

Huawei's new 3D lock screen wallpaper is actually pretty interesting. Unlike iPhone's Spatial Photo, which creates a fixed stereoscopic depth effect from a photo, this lets you scan an object 360°, generate a full 3D Gaussian Splatting asset, and use that as your lock screen. Tilt the phone and you're moving through a reconstructed 3D scene, not a fixed stereo image. The part worth paying attention to: 3D capture moving out of creative pipelines and into everyday phone personalization. That's a different kind of adoption curve.

KIRI Engine - 3D Scanner App

186,576 просмотров • 3 месяцев назад

gemini 3 is unbelievable it can create an app that turns image into 3D interactive The Matrix scene, adjust particle amount, color, density interact with particles using mouse or hands created with text prompts, no code needed
0:16

gemini 3 is unbelievable it can create an app that turns image into 3D interactive The Matrix scene, adjust particle amount, color, density interact with particles using mouse or hands created with text prompts, no code needed

el.cine

1,845,176 просмотров • 10 месяцев назад

Hyper3D Rodin is built for generating, editing, and refining 3D assets. Most segmentation tools leave rough edges, broken parts, and extra cleanup before the model is usable. Bang to Parts understands the object first, then turns one complete model into clean, editable components.
0:19

Hyper3D Rodin is built for generating, editing, and refining 3D assets. Most segmentation tools leave rough edges, broken parts, and extra cleanup before the model is usable. Bang to Parts understands the object first, then turns one complete model into clean, editable components.

AI Frontliner

14,536 просмотров • 1 месяц назад

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)”
0:18

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)”

Bilawal Sidhu

92,919 просмотров • 2 лет назад

🚨BREAKING: AI 3D just crossed into anime territory. We tested Tripo AI on a masked warrior and a mech guardian. Single image to full 3D asset. Here is what came out:
0:19

🚨BREAKING: AI 3D just crossed into anime territory. We tested Tripo AI on a masked warrior and a mech guardian. Single image to full 3D asset. Here is what came out:

The AI Colony

23,031 просмотров • 3 месяцев назад

🤩Video world models are cool, but it is cooler if they can simulate any 3D physical actions in real time! Introducing RealWonder⚡️: Now you can simulate 3D physical action (robot actions, 3D forces, force fields, etc.) consequences from a single image in real time! 🧵1/6
0:10

🤩Video world models are cool, but it is cooler if they can simulate any 3D physical actions in real time! Introducing RealWonder⚡️: Now you can simulate 3D physical action (robot actions, 3D forces, force fields, etc.) consequences from a single image in real time! 🧵1/6

Hong-Xing (Koven) Yu

30,524 просмотров • 7 месяцев назад