Loading video...

Video Failed to Load

Go Home

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

_akhaliq's profile picture

AK

516,607 subscribers

29,617 views • 1 year 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

17,408 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

10,631 views • 2 years ago

Motion practice in Cinema 4d
0:17

Motion practice in Cinema 4d

Buttery

34,753 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

31,824 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

43,382 views • 6 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 • 5 months ago

ReViV reconstructs viewer-centric human motion (body, hand, and gaze) and view-centric scene geometry (camera and depth) from a single egocentric RGB video in a unified feed-forward model. It formulates the task as learning the full joint probability distribution over multimodal signals, including RGB video, camera trajectory, gaze direction, full-body motion, hand motion, and depth. Powered by a Masked Generative Egocentric Transformer, ReViV operates within a single feed-forward architecture to simultaneously reconstruct the temporally consistent 4D reconstruction across the viewer and the view with fast inference speed. Paper Title: ReViV: Reconstructing the Viewer and the View in 4D from Monocular Project: Link:
0:14

ReViV reconstructs viewer-centric human motion (body, hand, and gaze) and view-centric scene geometry (camera and depth) from a single egocentric RGB video in a unified feed-forward model. It formulates the task as learning the full joint probability distribution over multimodal signals, including RGB video, camera trajectory, gaze direction, full-body motion, hand motion, and depth. Powered by a Masked Generative Egocentric Transformer, ReViV operates within a single feed-forward architecture to simultaneously reconstruct the temporally consistent 4D reconstruction across the viewer and the view with fast inference speed. Paper Title: ReViV: Reconstructing the Viewer and the View in 4D from Monocular Project: Link:

AI Bites | YouTube Channel

18,590 views • 19 days 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

4,430,950 views • 2 years ago

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

Made it in one single prompt in Cinema 4D 😎

Anton Skvortsov

52,915 views • 3 months 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

55,223 views • 4 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 • 1 year 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 • 1 year ago

WeatherEdit: Controllable Weather Editing with 4D Gaussian Field Contributions: 1. Based on our analysis of weather editing characteristics, we introduce WeatherEdit, a comprehensive and efficient framework for realistic and controllable weather generation. Compared with existing methods that focus on either background editing or static weather effects, a progressive 2D-to-4D transformation process in WeatherEdit enhances adaptability across a wider range of scenarios. 2. We introduce an all-in-one adapter to enable a diffusion model for multi-weather (snowy, rainy, and fog) synthesis, along with a Temporal-View attention to ensure consistent editing across multi-frame and multi-view. 3. We design a 4D Gaussian field for weather particle modeling, enabling plausible simulation of raindrops, snowflakes, and fog with controllable severity. 4. We demonstrate WeatherEdit’s effectiveness in generating realistic, consistent, and controllable weather effects in 3D driving scenes, showcasing its applicability to real-world scenarios.
0:13

WeatherEdit: Controllable Weather Editing with 4D Gaussian Field Contributions: 1. Based on our analysis of weather editing characteristics, we introduce WeatherEdit, a comprehensive and efficient framework for realistic and controllable weather generation. Compared with existing methods that focus on either background editing or static weather effects, a progressive 2D-to-4D transformation process in WeatherEdit enhances adaptability across a wider range of scenarios. 2. We introduce an all-in-one adapter to enable a diffusion model for multi-weather (snowy, rainy, and fog) synthesis, along with a Temporal-View attention to ensure consistent editing across multi-frame and multi-view. 3. We design a 4D Gaussian field for weather particle modeling, enabling plausible simulation of raindrops, snowflakes, and fog with controllable severity. 4. We demonstrate WeatherEdit’s effectiveness in generating realistic, consistent, and controllable weather effects in 3D driving scenes, showcasing its applicability to real-world scenarios.

MrNeRF

10,691 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 • 1 year ago

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 views • 1 year ago

All in one motion 👏
0:12

All in one motion 👏

Aston Villa

27,904 views • 1 year ago

Just added In View component to motion-primitives. Easily trigger animations when elements are in the viewport
0:10

Just added In View component to motion-primitives. Easily trigger animations when elements are in the viewport

Ibelick

10,365 views • 2 years ago