Loading video...

Video Failed to Load

Go Home

MoVieS Motion-Aware 4D Dynamic View Synthesis in One Second

_akhaliq's profile picture

AK

504,364 subscribers

29,601 views • 11 months ago •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments TL;DR: We present WildGS-SLAM, a robust monocular RGB SLAM system. - Utilizes uncertainty-aware tracking and mapping to handle dynamic scenes. - Leverages DINOv2-based uncertainty maps for dynamic object removal. - Improves tracking and mapping. - Enables high-quality view synthesis.
0:19

WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments TL;DR: We present WildGS-SLAM, a robust monocular RGB SLAM system. - Utilizes uncertainty-aware tracking and mapping to handle dynamic scenes. - Leverages DINOv2-based uncertainty maps for dynamic object removal. - Improves tracking and mapping. - Enables high-quality view synthesis.

MrNeRF

17,382 views • 1 year ago

We're excited to introduce a new dataset, 4D-DRESS, at #CVPR2024. It features diverse human subjects, realistic clothing deformations, and dynamic body motion in 4D. (1/6) 🧵👇
0:18

We're excited to introduce a new dataset, 4D-DRESS, at #CVPR2024. It features diverse human subjects, realistic clothing deformations, and dynamic body motion in 4D. (1/6) 🧵👇

AIT Lab

10,630 views • 2 years ago

Motion practice in Cinema 4d
0:17

Motion practice in Cinema 4d

Buttery

34,709 views • 2 years ago

Adaptive and Temporally Consistent Gaussian Surfels for Multi-view Dynamic Reconstruction Contributions: • A method for efficiently reconstructing dynamic surfaces from multi-view videos using Gaussian surfels. • A unified and gradient-aware densification strategy for optimizing dynamic 3D Gaussians with fine details. • A temporal consistency approach that ensures stable and coherent surface reconstructions across frames by enforcing consistency on curvature maps. • Extensive experiments that demonstrate our method’s advantages including fast training, high-fidelity novel view synthesis, and accurate surface geometry.
0:11

Adaptive and Temporally Consistent Gaussian Surfels for Multi-view Dynamic Reconstruction Contributions: • A method for efficiently reconstructing dynamic surfaces from multi-view videos using Gaussian surfels. • A unified and gradient-aware densification strategy for optimizing dynamic 3D Gaussians with fine details. • A temporal consistency approach that ensures stable and coherent surface reconstructions across frames by enforcing consistency on curvature maps. • Extensive experiments that demonstrate our method’s advantages including fast training, high-fidelity novel view synthesis, and accurate surface geometry.

MrNeRF

31,821 views • 1 year ago

✨ Any static 3D assets ➡️ 4D dynamic worlds. Introducing CHORD, a universal framework for generating scene-level 4D dynamic motion from any static 3D inputs. It generalizes surprisingly well across a wide range of objects 🤯 and can even be used to learn robotics manipulation policy 🤖! Project page: Dive deeper in a 🧵: 1/n
0:12

✨ Any static 3D assets ➡️ 4D dynamic worlds. Introducing CHORD, a universal framework for generating scene-level 4D dynamic motion from any static 3D inputs. It generalizes surprisingly well across a wide range of objects 🤯 and can even be used to learn robotics manipulation policy 🤖! Project page: Dive deeper in a 🧵: 1/n

Chen Geng

43,033 views • 5 months ago

🤖 One predictive backbone, three distinct tasks, consistent gains: a strong signal that investing in reusable world models is the right abstraction for robot learning. Toyota Research Institute (TRI) just ran the same idea three different ways—and it worked each time. Using our NVIDIA Cosmos Predict 2–style world models, they hit SOTA with dynamic view synthesis, teleop data augmentation (even up against MimicGen as an upper bound), and navigation world models. 🙌 📚 dynamic view synthesis 📚 teleop data augmentation (also compare to MimicGen as an upper bound) 📚 navigation world models 📸 from
0:16

🤖 One predictive backbone, three distinct tasks, consistent gains: a strong signal that investing in reusable world models is the right abstraction for robot learning. Toyota Research Institute (TRI) just ran the same idea three different ways—and it worked each time. Using our NVIDIA Cosmos Predict 2–style world models, they hit SOTA with dynamic view synthesis, teleop data augmentation (even up against MimicGen as an upper bound), and navigation world models. 🙌 📚 dynamic view synthesis 📚 teleop data augmentation (also compare to MimicGen as an upper bound) 📚 navigation world models 📸 from

NVIDIA AI Developer

38,100 views • 4 months ago

Introducing Make-A-Video3D! Generating 3D dynamic (mini) scenes from input text. That is, text --> 4D! Needs no 4D data (i.e., no dynamic 3D data), no static 3D data, no paired text-video data. Paper: Website:
0:16

Introducing Make-A-Video3D! Generating 3D dynamic (mini) scenes from input text. That is, text --> 4D! Needs no 4D data (i.e., no dynamic 3D data), no static 3D data, no paired text-video data. Paper: Website:

Devi Parikh

305,725 views • 3 years ago

still one of the most detailed stop motion movies ever made.
0:17

still one of the most detailed stop motion movies ever made.

The Fairy Art Mother

4,428,373 views • 1 year ago

Lower body dynamic motion showcase #TheFirstDescendant
0:13

Lower body dynamic motion showcase #TheFirstDescendant

The First Descendant Fans

80,825 views • 1 year ago

Made it in one single prompt in Cinema 4D 😎
0:12

Made it in one single prompt in Cinema 4D 😎

Anton Skvortsov

51,808 views • 1 month ago

Text-To-4D Dynamic Scene Generation Presents MAV3D (Make-A-Video3D), a method for generating three-dimensional dynamic scenes from text descriptions. proj: abs:
0:11

Text-To-4D Dynamic Scene Generation Presents MAV3D (Make-A-Video3D), a method for generating three-dimensional dynamic scenes from text descriptions. proj: abs:

Aran Komatsuzaki

54,664 views • 3 years ago

One second later, and this could’ve gone completely different… always be aware of your surroundings
0:10

One second later, and this could’ve gone completely different… always be aware of your surroundings

Interesting things

55,204 views • 2 months ago

The view of the accident in slow motion is incredible
0:17

The view of the accident in slow motion is incredible

Slow Motion Video

32,740 views • 11 months ago

SplatVoxel: History-Aware Novel View Streaming without Temporal Training Contributions: • We propose a hybrid Splat-Voxel feed-forward reconstruction framework that leverages historical information to enable novel view streaming, without relying on multi-view video datasets for training. • We develop an efficient sparse voxel transformer with a coarse-to-fine voxel representation, outperforming existing feed-forward Gaussian splatting methods. • Experiment results demonstrate that our proposed framework enhances novel view synthesis for streaming scene reconstruction, providing better visual quality and reduced temporal artifacts through history-aware modeling.
0:18

SplatVoxel: History-Aware Novel View Streaming without Temporal Training Contributions: • We propose a hybrid Splat-Voxel feed-forward reconstruction framework that leverages historical information to enable novel view streaming, without relying on multi-view video datasets for training. • We develop an efficient sparse voxel transformer with a coarse-to-fine voxel representation, outperforming existing feed-forward Gaussian splatting methods. • Experiment results demonstrate that our proposed framework enhances novel view synthesis for streaming scene reconstruction, providing better visual quality and reduced temporal artifacts through history-aware modeling.

MrNeRF

10,823 views • 1 year ago

Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models Contributions: • We introduce Diffuman4D, a novel diffusion model that generates spatio-temporally consistent and high-resolution (1024p) human videos from sparse-view video inputs. • We propose a sliding iterative denoising mechanism that enhances both the spatial and temporal consistency of generated long-term videos while maintaining efficient inference. • We design a human pose conditioning scheme to enhance the appearance quality and motion accuracy of generated human videos. • We plan to release our processed version of the DNA-Rendering dataset, which we believe will benefit future research in this area.
0:10

Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models Contributions: • We introduce Diffuman4D, a novel diffusion model that generates spatio-temporally consistent and high-resolution (1024p) human videos from sparse-view video inputs. • We propose a sliding iterative denoising mechanism that enhances both the spatial and temporal consistency of generated long-term videos while maintaining efficient inference. • We design a human pose conditioning scheme to enhance the appearance quality and motion accuracy of generated human videos. • We plan to release our processed version of the DNA-Rendering dataset, which we believe will benefit future research in this area.

MrNeRF

24,729 views • 11 months ago

#UltimateBunny 😮‍💨 She was jiggling before the dynamic motion update😌
0:13

#UltimateBunny 😮‍💨 She was jiggling before the dynamic motion update😌

RAW

28,731 views • 1 year ago

Poetry in motion ✨ Our second 1.94 second pit stop from the race 😍 #McLaren | #HungarianGP 🇭🇺
0:12

Poetry in motion ✨ Our second 1.94 second pit stop from the race 😍 #McLaren | #HungarianGP 🇭🇺

McLaren

80,075 views • 10 months ago

The one 4D ride came to mind
0:16

The one 4D ride came to mind

#1 Space Jam 2 Lover

663,794 views • 6 months ago