Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Dream2Flow Bridging Video Generation and Open-World Manipulation with 3D Object Flow

_akhaliq's profile picture

AK

449,616 subscribers

29,980 görüntüleme • 5 ay önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

What representation enables open-world robot manipulation from generated videos? Introducing Dream2Flow, our recent work that bridges video generation and robot control with 3D object flow. Stanford University #ICRA2026 1/N
1:08

What representation enables open-world robot manipulation from generated videos? Introducing Dream2Flow, our recent work that bridges video generation and robot control with 3D object flow. Stanford University #ICRA2026 1/N

Wenlong Huang

105,597 görüntüleme • 3 ay önce

Nvidia just dropped GEN3C 3D-Informed World-Consistent Video Generation with Precise Camera Control
1:18

Nvidia just dropped GEN3C 3D-Informed World-Consistent Video Generation with Precise Camera Control

AK

58,157 görüntüleme • 1 yıl önce

InsertAnywhere Bridging 4D Scene Geometry and Diffusion Models for Realistic Video Object Insertion
0:21

InsertAnywhere Bridging 4D Scene Geometry and Diffusion Models for Realistic Video Object Insertion

AK

26,123 görüntüleme • 6 ay önce

Voyager Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation
0:30

Voyager Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation

AK

15,840 görüntüleme • 1 yıl önce

New Kling, Runway, Luma competitor? Open text and image video generation model Pyramidal Flow Matching for Efficient Video Generative Modeling
2:23

New Kling, Runway, Luma competitor? Open text and image video generation model Pyramidal Flow Matching for Efficient Video Generative Modeling

AK

90,965 görüntüleme • 1 yıl önce

Nano Banana 2, new state of the art in image generation and editing combined with Gemini’s real-world knowledge! You can simulate 3D CAD models purely through images. From sketch to real object!
0:22

Nano Banana 2, new state of the art in image generation and editing combined with Gemini’s real-world knowledge! You can simulate 3D CAD models purely through images. From sketch to real object!

koray kavukcuoglu

85,482 görüntüleme • 4 ay 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:
0:46

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

33,363 görüntüleme • 1 yıl önce

ICRA2025: OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding
0:29

ICRA2025: OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding

rsasaki0109

13,856 görüntüleme • 1 yıl önce

We’re open-sourcing HY-World 2.0, a multimodal world model that generates, reconstructs, and simulates interactive *3D worlds* from text, images, and videos. Outputs can be integrated into game engines and embodied simulation pipelines. Key highlights: 🔹 One-click world generation Turn text or image into interactive 3D worlds automatically. 🔹 Pipeline-ready 3D outputs Editable 3D worlds for Unity and Unreal Engine, with standard 3D exports including mesh, 3DGS, and point clouds. 🔹 Unified world model system One model family for world generation and reconstruction across synthetic and real-world scenes. 🔹 Interactive character mode Explore generated 3D worlds in real time with physics-aware movement and collision support. ✨ Apply for access: 🔗 GitHub: 🤗 Hugging Face: 📄 Technical Report:
1:04

We’re open-sourcing HY-World 2.0, a multimodal world model that generates, reconstructs, and simulates interactive *3D worlds* from text, images, and videos. Outputs can be integrated into game engines and embodied simulation pipelines. Key highlights: 🔹 One-click world generation Turn text or image into interactive 3D worlds automatically. 🔹 Pipeline-ready 3D outputs Editable 3D worlds for Unity and Unreal Engine, with standard 3D exports including mesh, 3DGS, and point clouds. 🔹 Unified world model system One model family for world generation and reconstruction across synthetic and real-world scenes. 🔹 Interactive character mode Explore generated 3D worlds in real time with physics-aware movement and collision support. ✨ Apply for access: 🔗 GitHub: 🤗 Hugging Face: 📄 Technical Report:

Tencent Hy

369,643 görüntüleme • 2 ay önce

We are thrilled to announce a major upgrade to our open-source 3D generation model, introducing two groundbreaking new versions: 3D 2.0 MV (Multi-View Generation) and 3D 2.0 Mini! 3D 2.0 MV : 3D 2.0Mini:
0:39

We are thrilled to announce a major upgrade to our open-source 3D generation model, introducing two groundbreaking new versions: 3D 2.0 MV (Multi-View Generation) and 3D 2.0 Mini! 3D 2.0 MV : 3D 2.0Mini:

Tencent HY

134,410 görüntüleme • 1 yıl önce

open_semantic_slam ICRA2025: OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding
0:29

open_semantic_slam ICRA2025: OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding

rsasaki0109

27,137 görüntüleme • 1 yıl önce

🥳 The launches continue with a brand new feature: Editing your videos directly in Flow. Starting with object insertion, just select where you want your object and describe it. Flow will then both generate the object *and* seamlessly integrate it into your clip.
0:29

🥳 The launches continue with a brand new feature: Editing your videos directly in Flow. Starting with object insertion, just select where you want your object and describe it. Flow will then both generate the object *and* seamlessly integrate it into your clip.

FlowbyGoogle

34,223 görüntüleme • 8 ay önce

HunyuanWorld-Voyager is here and fully open-source! The world’s first ultra-long-range world model with native 3D reconstruction, redefining AI-driven spatial intelligence for VR, gaming, and simulations. ✅Direct 3D Output: Exports point cloud videos to 3D formats without tools like COLMAP, enabling instant 3D application use. ✅Innovative 3D Memory: Introduces a scalable world caching mechanism, ensuring geometric consistency across any camera trajectory. ✅Top-Ranked Performance: #1 on Stanford’s WorldScore, excelling in video generation and 3D reconstruction benchmarks.( Built on HunyuanWorld 1.0, Voyager blends video generation with 3D modeling, delivering camera-controlled, high-fidelity RGB-D sequences. Control scenes via keyboard or joystick for unmatched 3D consistency. Explore now: 🌐Project Page: 🔗GitHub: 🤗HuggingFace: 📝Technical Details:
0:37

HunyuanWorld-Voyager is here and fully open-source! The world’s first ultra-long-range world model with native 3D reconstruction, redefining AI-driven spatial intelligence for VR, gaming, and simulations. ✅Direct 3D Output: Exports point cloud videos to 3D formats without tools like COLMAP, enabling instant 3D application use. ✅Innovative 3D Memory: Introduces a scalable world caching mechanism, ensuring geometric consistency across any camera trajectory. ✅Top-Ranked Performance: #1 on Stanford’s WorldScore, excelling in video generation and 3D reconstruction benchmarks.( Built on HunyuanWorld 1.0, Voyager blends video generation with 3D modeling, delivering camera-controlled, high-fidelity RGB-D sequences. Control scenes via keyboard or joystick for unmatched 3D consistency. Explore now: 🌐Project Page: 🔗GitHub: 🤗HuggingFace: 📝Technical Details:

Tencent Hy

198,207 görüntüleme • 10 ay önce

"MeshFlow: Efficient Artistic Mesh Generation via MeshVAE and Flow-based Diffusion Transformer" TL;DR: learns a continuous mesh latent space and generates vertices and connectivity in parallel with flow matching, producing quality 3D meshes up to 18× faster than autoregressive.
0:29

"MeshFlow: Efficient Artistic Mesh Generation via MeshVAE and Flow-based Diffusion Transformer" TL;DR: learns a continuous mesh latent space and generates vertices and connectivity in parallel with flow matching, producing quality 3D meshes up to 18× faster than autoregressive.

Alexandre Morgand

42,638 görüntüleme • 17 gün önce

Inspire your inner storyteller. With Flow, you can generate images and videos, effortlessly bridging the gap between concept and creation.
0:25

Inspire your inner storyteller. With Flow, you can generate images and videos, effortlessly bridging the gap between concept and creation.

Google Flow

14,774 görüntüleme • 1 ay önce

WorldFlow3D: Unbounded 3D World Generation 🌍 by Flow Through Hierarchical Distributions, without VAEs ! We reformulate 3D generation as flowing through sequentially finer 3D distributions, cutting training time by more than half ⏱️ compared to existing approaches! Vectorized map layouts provide full scene controllability 🗺️, and a novel flow-field alignment process enables causally coherent, spatially unbounded generation 🌍. This generative method generalizes across both real and synthetic data distributions! Project: Project led by Amogh Joshi and Julian Ost — will be super fun to build on this! 🔥
0:53

WorldFlow3D: Unbounded 3D World Generation 🌍 by Flow Through Hierarchical Distributions, without VAEs ! We reformulate 3D generation as flowing through sequentially finer 3D distributions, cutting training time by more than half ⏱️ compared to existing approaches! Vectorized map layouts provide full scene controllability 🗺️, and a novel flow-field alignment process enables causally coherent, spatially unbounded generation 🌍. This generative method generalizes across both real and synthetic data distributions! Project: Project led by Amogh Joshi and Julian Ost — will be super fun to build on this! 🔥

Felix Heide

19,473 görüntüleme • 2 ay önce

update: enhanced 3d world generation just dropped in Teleport with World Labs marble 1.1 16 iPhone photos → fully explorable 3d world in minutes 📸 golden gai, shinjuku 🇯🇵
0:30

update: enhanced 3d world generation just dropped in Teleport with World Labs marble 1.1 16 iPhone photos → fully explorable 3d world in minutes 📸 golden gai, shinjuku 🇯🇵

Martin Maly

115,775 görüntüleme • 2 ay önce

An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion discuss: We introduce a new approach for generating realistic 3D models with UV maps through a representation termed "Object Images." This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively converting complex 3D shapes into a more manageable 2D format. By doing so, we address the challenges of both geometric and semantic irregularity inherent in polygonal meshes. This method allows us to use image generation models, such as Diffusion Transformers, directly for 3D shape generation. Evaluated on the ABO dataset, our generated shapes with patch structures achieve point cloud FID comparable to recent 3D generative models, while naturally supporting PBR material generation.
0:34

An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion discuss: We introduce a new approach for generating realistic 3D models with UV maps through a representation termed "Object Images." This approach encapsulates surface geometry, appearance, and patch structures within a 64x64 pixel image, effectively converting complex 3D shapes into a more manageable 2D format. By doing so, we address the challenges of both geometric and semantic irregularity inherent in polygonal meshes. This method allows us to use image generation models, such as Diffusion Transformers, directly for 3D shape generation. Evaluated on the ABO dataset, our generated shapes with patch structures achieve point cloud FID comparable to recent 3D generative models, while naturally supporting PBR material generation.

AK

66,412 görüntüleme • 1 yıl önce

Introducing ShapeR, a method for robust conditional 3D shape generation from casually captured sequences. ShapeR leverages a rectified flow transformer conditioned on per-object multimodal data to turn casual image sequences into full metric scene reconstructions. Project Page: Paper: Links to code and huggingface below ⬇️
0:58

Introducing ShapeR, a method for robust conditional 3D shape generation from casually captured sequences. ShapeR leverages a rectified flow transformer conditioned on per-object multimodal data to turn casual image sequences into full metric scene reconstructions. Project Page: Paper: Links to code and huggingface below ⬇️

Yawar Siddiqui

70,577 görüntüleme • 5 ay önce