Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

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

_akhaliq's profile picture

AK

450,730 subscribers

26,123 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

Google presents CAT4D Create Anything in 4D with Multi-View Video Diffusion Models
2:12

Google presents CAT4D Create Anything in 4D with Multi-View Video Diffusion Models

AK

61,949 görüntüleme • 1 yıl önce

🚀 Introducing DiffusionRenderer, a neural rendering engine powered by video diffusion models. 🎥 Estimates high-quality geometry and materials from videos, synthesizes photorealistic light transport, enables relighting and material editing with realistic shadows and reflections
2:14

🚀 Introducing DiffusionRenderer, a neural rendering engine powered by video diffusion models. 🎥 Estimates high-quality geometry and materials from videos, synthesizes photorealistic light transport, enables relighting and material editing with realistic shadows and reflections

Zian Wang

125,348 görüntüleme • 1 yıl önce

We’ve upgraded Stable Video Diffusion 4D to Stable Video 4D 2.0 (SV4D 2.0), improving the quality of 4D outputs generated from a single object-centric video. While 3D provides a static view of an object’s shape and size; 4D extends this by including time, showing how the object moves. This multi-view video diffusion model generates a 4D output in three steps: 1️⃣ Starts with an input video of a moving person or object 2️⃣ Generates novel views of the subject from unseen angles 3️⃣ Constructs a single dynamic 4D video output with spatial and temporal consistency You can learn more here: (1/4)
0:30

We’ve upgraded Stable Video Diffusion 4D to Stable Video 4D 2.0 (SV4D 2.0), improving the quality of 4D outputs generated from a single object-centric video. While 3D provides a static view of an object’s shape and size; 4D extends this by including time, showing how the object moves. This multi-view video diffusion model generates a 4D output in three steps: 1️⃣ Starts with an input video of a moving person or object 2️⃣ Generates novel views of the subject from unseen angles 3️⃣ Constructs a single dynamic 4D video output with spatial and temporal consistency You can learn more here: (1/4)

Stability AI

35,974 görüntüleme • 1 yıl önce

Nvidia just announced Align Your Gaussians Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models
0:22

Nvidia just announced Align Your Gaussians Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models

AK

131,297 görüntüleme • 2 yıl önce

PAIR-Diffusion: Object-Level Image Editing with Structure-and-Appearance Paired Diffusion Models Gradio demo is out on Hugging Face Spaces demo:
1:05

PAIR-Diffusion: Object-Level Image Editing with Structure-and-Appearance Paired Diffusion Models Gradio demo is out on Hugging Face Spaces demo:

AK

87,679 görüntüleme • 3 yıl önce

Diffuman4D 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models
0:58

Diffuman4D 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models

AK

12,518 görüntüleme • 11 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

Oh wow! "[SIGGRAPH '24] DreamMat: High-quality PBR Material Generation with Geometry- and Light-aware Diffusion Models" Paper: Project: Code (MIT) :
1:04

Oh wow! "[SIGGRAPH '24] DreamMat: High-quality PBR Material Generation with Geometry- and Light-aware Diffusion Models" Paper: Project: Code (MIT) :

MrNeRF

59,480 görüntüleme • 2 yıl önce

Dream2Flow Bridging Video Generation and Open-World Manipulation with 3D Object Flow
2:59

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

AK

29,980 görüntüleme • 5 ay önce

Zhou et al., "PAGE-4D: Disentangled Pose and Geometry Estimation for 4D Perception" VGGT extended to dynamic scenes with a dynamic mask predictor.
0:25

Zhou et al., "PAGE-4D: Disentangled Pose and Geometry Estimation for 4D Perception" VGGT extended to dynamic scenes with a dynamic mask predictor.

Kwang Moo Yi

11,492 görüntüleme • 7 ay önce

Check out Christian Diller's CG-HOI :) We generate realistic 3D human-object interactions, from object geometry and text description. A key ingredient is explicit modeling of contact, during training and as guidance during inference.
0:31

Check out Christian Diller's CG-HOI :) We generate realistic 3D human-object interactions, from object geometry and text description. A key ingredient is explicit modeling of contact, during training and as guidance during inference.

Angela Dai

20,497 görüntüleme • 2 yıl önce

Feature4X Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields
1:16

Feature4X Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields

AK

13,892 görüntüleme • 1 yıl 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

VerseCrafter Dynamic Realistic Video World Model with 4D Geometric Control
0:44

VerseCrafter Dynamic Realistic Video World Model with 4D Geometric Control

AK

26,856 görüntüleme • 5 ay önce

Block Diffusion Interpolating Between Autoregressive and Diffusion Language Models
0:23

Block Diffusion Interpolating Between Autoregressive and Diffusion Language Models

AK

160,553 görüntüleme • 1 yıl önce

Google announces InseRF Text-Driven Generative Object Insertion in Neural 3D Scenes paper page: InseRF generates an object in a 3D scene via a text prompt and one 2D bounding box
1:35

Google announces InseRF Text-Driven Generative Object Insertion in Neural 3D Scenes paper page: InseRF generates an object in a 3D scene via a text prompt and one 2D bounding box

AK

205,943 görüntüleme • 2 yıl önce

Excited to share MonST3R! -- a simple way to estimate geometry from unposed video of dynamic scene We achieve competitive results on several downstreams (video depth, camera pose) and believe this is a promising step toward feed-forward 4D reconstruction
1:03

Excited to share MonST3R! -- a simple way to estimate geometry from unposed video of dynamic scene We achieve competitive results on several downstreams (video depth, camera pose) and believe this is a promising step toward feed-forward 4D reconstruction

Junyi Zhang @CVPR

131,523 görüntüleme • 1 yıl önce

Nvidia presents Articulated Kinematics Distillation from Video Diffusion Models
4:11

Nvidia presents Articulated Kinematics Distillation from Video Diffusion Models

AK

39,189 görüntüleme • 1 yıl önce