Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

V-DPM 4D Video Reconstruction with Dynamic Point Maps

_akhaliq's profile picture

AK

480,615 subscribers

19,274 görüntüleme • 4 ay önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

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

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

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

Check out 𝐌𝐨𝐭𝐢𝐨𝐧𝟐𝐕𝐞𝐜𝐒𝐞𝐭𝐬, a 4D diffusion model for dynamic surface reconstruction from imperfect observations of sparse, noisy, or partial point clouds. Main idea: we represent time-varying shapes via 4D neural representation with latent vector sets, and then explicitly learns the shape and motion distribution of non-rigid objects through an iterative denoising process of latent vector sets. Great work by Wei Cao るちゃん Biao Zhang Jiapeng Tang
0:32

Check out 𝐌𝐨𝐭𝐢𝐨𝐧𝟐𝐕𝐞𝐜𝐒𝐞𝐭𝐬, a 4D diffusion model for dynamic surface reconstruction from imperfect observations of sparse, noisy, or partial point clouds. Main idea: we represent time-varying shapes via 4D neural representation with latent vector sets, and then explicitly learns the shape and motion distribution of non-rigid objects through an iterative denoising process of latent vector sets. Great work by Wei Cao るちゃん Biao Zhang Jiapeng Tang

Matthias Niessner

29,058 görüntüleme • 2 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

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

🔥Unbounded 4D City Generation🔥 #CityDreamer4D is a generative model of unbounded 4D cities that decouples static and dynamic scenes with realism and controllability - Project: - Code: - CityTopia Dataset:
1:02

🔥Unbounded 4D City Generation🔥 #CityDreamer4D is a generative model of unbounded 4D cities that decouples static and dynamic scenes with realism and controllability - Project: - Code: - CityTopia Dataset:

Ziwei Liu

24,326 görüntüleme • 1 yıl önce

4DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videos Abstract: We propose 4DGT, a 4D Gaussian-based Transformer model for dynamic scene reconstruction, trained entirely on real-world monocular posed videos. Using 4D Gaussian as an inductive bias, 4DGT unifies static and dynamic components, enabling the modeling of complex, time-varying environments with varying object lifespans. We introduced a novel density control strategy in training, which allows our 4DGT to handle longer space-time input while maintaining efficient rendering at runtime. Our model processes 64 consecutive posed frames in a rolling-window fashion, predicting consistent 4D Gaussians in the scene. Unlike optimization-based methods, 4DGT performs purely feed-forward inference, reducing reconstruction time from hours to seconds and scaling effectively to long video sequences. Trained only on large-scale monocular posed video datasets, 4DGT can significantly outperform prior Gaussian-based networks in real-world videos and achieve on-par accuracy with optimization-based methods on cross-domain videos.
5:19

4DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videos Abstract: We propose 4DGT, a 4D Gaussian-based Transformer model for dynamic scene reconstruction, trained entirely on real-world monocular posed videos. Using 4D Gaussian as an inductive bias, 4DGT unifies static and dynamic components, enabling the modeling of complex, time-varying environments with varying object lifespans. We introduced a novel density control strategy in training, which allows our 4DGT to handle longer space-time input while maintaining efficient rendering at runtime. Our model processes 64 consecutive posed frames in a rolling-window fashion, predicting consistent 4D Gaussians in the scene. Unlike optimization-based methods, 4DGT performs purely feed-forward inference, reducing reconstruction time from hours to seconds and scaling effectively to long video sequences. Trained only on large-scale monocular posed video datasets, 4DGT can significantly outperform prior Gaussian-based networks in real-world videos and achieve on-par accuracy with optimization-based methods on cross-domain videos.

MrNeRF

34,782 görüntüleme • 1 yıl önce

🚀We are thrilled to release an alpha version of OnePoseViaGen - a system for Panoptic 4D Scene Reconstruction from RGB video! 👉Try it at : #AI #3D #GenerativeAI
0:30

🚀We are thrilled to release an alpha version of OnePoseViaGen - a system for Panoptic 4D Scene Reconstruction from RGB video! 👉Try it at : #AI #3D #GenerativeAI

Chongjie(CJ) Ye

23,136 görüntüleme • 10 ay önce

Check out this BIM 4D sim by Thiery Peleias, owner of Virtuart4d 🚧 Imported into UE5 for the animation aspect, the project model was created in Revit, the 4D planning animation in Synchro 4D Pro and the environment captured from Cesium with Google Maps Photorealistic 3D Tiles!
1:47

Check out this BIM 4D sim by Thiery Peleias, owner of Virtuart4d 🚧 Imported into UE5 for the animation aspect, the project model was created in Revit, the 4D planning animation in Synchro 4D Pro and the environment captured from Cesium with Google Maps Photorealistic 3D Tiles!

Unreal Engine

20,772 görüntüleme • 1 yıl önce

Introducing VGGT-Ω: scaling feed-forward reconstruction across static and dynamic scenes, and studying whether the learned geometric representations transfer beyond reconstruction.
0:48

Introducing VGGT-Ω: scaling feed-forward reconstruction across static and dynamic scenes, and studying whether the learned geometric representations transfer beyond reconstruction.

Jianyuan

776,966 görüntüleme • 26 gün önce

Watch the new exclusive trailer for Sea of Remnants, launching with DLSS 4.5 Super Resolution, Ray Reconstruction, and path tracing. Brave the dynamic seas with #RTXOn →
0:50

Watch the new exclusive trailer for Sea of Remnants, launching with DLSS 4.5 Super Resolution, Ray Reconstruction, and path tracing. Brave the dynamic seas with #RTXOn →

NVIDIA GeForce

51,576 görüntüleme • 3 ay önce

Robot Learning needs 4D world models! Robot Learning needs 4D world models! Robot Learning needs 4D world models! We introduce TesserAct, a 4D embodied world model that can simulate how agents interact with the 3D world over time! We achieve this by simply extending a pre-trained 2D video generation model to jointly predict RGB, depth, and surface normals. It enables: 1️⃣ Much better policy learning in the wild 2️⃣ Temporal + spatial coherence in 4D dynamic prediction 3️⃣ Novel view synthesis for embodied scenes Code: Paper Link: Project page:
0:32

Robot Learning needs 4D world models! Robot Learning needs 4D world models! Robot Learning needs 4D world models! We introduce TesserAct, a 4D embodied world model that can simulate how agents interact with the 3D world over time! We achieve this by simply extending a pre-trained 2D video generation model to jointly predict RGB, depth, and surface normals. It enables: 1️⃣ Much better policy learning in the wild 2️⃣ Temporal + spatial coherence in 4D dynamic prediction 3️⃣ Novel view synthesis for embodied scenes Code: Paper Link: Project page:

Chuang Gan

43,265 görüntüleme • 1 yıl önce

Light-X Generative 4D Video Rendering with Camera and Illumination Control
1:00

Light-X Generative 4D Video Rendering with Camera and Illumination Control

AK

31,126 görüntüleme • 6 ay önce

Introducing St4RTrack!🖖 Simultaneous 4D Reconstruction and Tracking in the world coordinate feed-forwardly, just by changing the meaning of two pointmaps!
1:57

Introducing St4RTrack!🖖 Simultaneous 4D Reconstruction and Tracking in the world coordinate feed-forwardly, just by changing the meaning of two pointmaps!

Junyi Zhang

52,426 görüntüleme • 1 yıl önce

VIDEO: Third Mainland Bridge requires either maintenance or reconstruction — with maintenance estimated at N3.8trn and reconstruction at N3.6trn, says Umahi
5:00

VIDEO: Third Mainland Bridge requires either maintenance or reconstruction — with maintenance estimated at N3.8trn and reconstruction at N3.6trn, says Umahi

TheCable

15,443 görüntüleme • 10 ay önce

EasyVolcap from Zhen Xu and Xiaowei Zhou is here! It's a unified open-source Pytorch library for accelerating neural volumetric video capturing, reconstruction, and rendering, akin to nerfstudio but tailored for 4D applications. 🔗
0:42

EasyVolcap from Zhen Xu and Xiaowei Zhou is here! It's a unified open-source Pytorch library for accelerating neural volumetric video capturing, reconstruction, and rendering, akin to nerfstudio but tailored for 4D applications. 🔗

Radiance Fields

10,611 görüntüleme • 2 yıl önce

Netflix+Wan+Qwen3-VL= DiffHDR. LDR-to-HDR video reconstruction. - uses Log-Gamma mapping to expand dynamic range; - Wan-2.1-VACE + LoRA; temporal stability and SOTA fidelity for professional video post-production.
1:14

Netflix+Wan+Qwen3-VL= DiffHDR. LDR-to-HDR video reconstruction. - uses Log-Gamma mapping to expand dynamic range; - Wan-2.1-VACE + LoRA; temporal stability and SOTA fidelity for professional video post-production.

Wildminder

25,969 görüntüleme • 2 ay önce