Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

🚀Excited to introduce GEN3C #CVPR2025, a generative video model with an explicit 3D cache for precise camera control. 🎥It applies to multiple use cases, including single-view and sparse-view NVS🖼️ and challenging settings like monocular dynamic NVS and driving simulation🚗. Project page:

Xuanchi Ren

1,426 subscribers

59,968 Aufrufe • vor 1 Jahr •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Stable Virtual Camera: Generative View Synthesis with Diffusion Models Hint: Check the project website. It is awesome! Contributions: 1. A training strategy for jointly modeling large viewpoint changes and temporal smoothness. 2. A two-pass procedural sampling method for smooth video generation along arbitrarily long camera trajectories. 3. A comprehensive benchmark that evaluates NVS methods across different datasets and settings. 4. An open-source release of model weights to support future research.

Stable Virtual Camera: Generative View Synthesis with Diffusion Models Hint: Check the project website. It is awesome! Contributions: 1. A training strategy for jointly modeling large viewpoint changes and temporal smoothness. 2. A two-pass procedural sampling method for smooth video generation along arbitrarily long camera trajectories. 3. A comprehensive benchmark that evaluates NVS methods across different datasets and settings. 4. An open-source release of model weights to support future research.

MrNeRF

38,619 Aufrufe • vor 1 Jahr

I'm excited to share our new work Align3R that estimates camera poses and consistent depth maps from a monocular video of a dynamic scene. Project page: Code: Paper:

I'm excited to share our new work Align3R that estimates camera poses and consistent depth maps from a monocular video of a dynamic scene. Project page: Code: Paper:

Yuan Liu

56,547 Aufrufe • vor 1 Jahr

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

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

AK

58,157 Aufrufe • vor 1 Jahr

Today, we are open-sourcing Hunyuan World 1.1 (WorldMirror), a universal feed-forward 3D reconstruction model. 🚀🚀🚀 While our previously released Hunyuan World 1.0 (open-sourced, lite version deployable on consumer GPUs) focused on generating 3D worlds from text or single-view images, Hunyuan World 1.1 significantly expands the input scope by unlocking video-to-3D and multi-view-to-3D world creation. Highlights: 🔹Any Input, Maximized Flexibility and Fidelity: Flexibly integrates diverse geometric priors (camera poses, intrinsics, depth maps) to resolve structural ambiguities and ensure geometrically consistent 3D outputs. 🔹Any Output, SOTA Results：This elegant architecture simultaneously generates multiple 3D representations: dense point clouds, multi-view depth maps, camera parameters, surface normals, and 3D Gaussian Splattings. 🔹Single-GPU & Fast Inference: As an all-in-one, feed-forward model, Hunyuan World 1.1 runs on a single GPU and delivers all 3D attributes in a single forward pass, within seconds. 🌐Project Page: 🔗Github： 🤗Hugging Face： ✨Demo: 📄Technical Report:

Today, we are open-sourcing Hunyuan World 1.1 (WorldMirror), a universal feed-forward 3D reconstruction model. 🚀🚀🚀 While our previously released Hunyuan World 1.0 (open-sourced, lite version deployable on consumer GPUs) focused on generating 3D worlds from text or single-view images, Hunyuan World 1.1 significantly expands the input scope by unlocking video-to-3D and multi-view-to-3D world creation. Highlights: 🔹Any Input, Maximized Flexibility and Fidelity: Flexibly integrates diverse geometric priors (camera poses, intrinsics, depth maps) to resolve structural ambiguities and ensure geometrically consistent 3D outputs. 🔹Any Output, SOTA Results：This elegant architecture simultaneously generates multiple 3D representations: dense point clouds, multi-view depth maps, camera parameters, surface normals, and 3D Gaussian Splattings. 🔹Single-GPU & Fast Inference: As an all-in-one, feed-forward model, Hunyuan World 1.1 runs on a single GPU and delivers all 3D attributes in a single forward pass, within seconds. 🌐Project Page: 🔗Github： 🤗Hugging Face： ✨Demo: 📄Technical Report:

Tencent Hy

168,451 Aufrufe • vor 8 Monaten

Generative Novel View Synthesis with 3D-Aware Diffusion Models abs: project page:

Generative Novel View Synthesis with 3D-Aware Diffusion Models abs: project page:

AK

304,708 Aufrufe • vor 3 Jahren

V3D Video Diffusion Models are Effective 3D Generators Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent advancements in video diffusion models, we introduce V3D, which leverages the world simulation capacity of pre-trained video diffusion models to facilitate 3D generation. To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator. Benefiting from this, the state-of-the-art video diffusion model could be fine-tuned to generate 360degree orbit frames surrounding an object given a single image. With our tailored reconstruction pipelines, we can generate high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method can be extended to scene-level novel view synthesis, achieving precise control over the camera path with sparse input views. Extensive experiments demonstrate the superior performance of the proposed approach, especially in terms of generation quality and multi-view consistency

V3D Video Diffusion Models are Effective 3D Generators Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent advancements in video diffusion models, we introduce V3D, which leverages the world simulation capacity of pre-trained video diffusion models to facilitate 3D generation. To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator. Benefiting from this, the state-of-the-art video diffusion model could be fine-tuned to generate 360degree orbit frames surrounding an object given a single image. With our tailored reconstruction pipelines, we can generate high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method can be extended to scene-level novel view synthesis, achieving precise control over the camera path with sparse input views. Extensive experiments demonstrate the superior performance of the proposed approach, especially in terms of generation quality and multi-view consistency

AK

31,997 Aufrufe • vor 2 Jahren

Speeding your view synthesis(<40s) with #InstantSplat! Our large-scale, pose-free method trains in just 37 seconds from sparse views—no #COLMAP, no intrinsics needed. Achieving nearly 30dB test PSNR with just 12 images, New standard in #NVS and new training efficiency. Project page 👉 Paper 📷:

Speeding your view synthesis(<40s) with #InstantSplat! Our large-scale, pose-free method trains in just 37 seconds from sparse views—no #COLMAP, no intrinsics needed. Achieving nearly 30dB test PSNR with just 12 images, New standard in #NVS and new training efficiency. Project page 👉 Paper 📷:

Zhiwen(Aaron) Fan

108,763 Aufrufe • vor 2 Jahren

and Wan2.2 again. MoCam - novel view synthesis. Lets you change the camera trajectory of a photo/video. - Video Re-camera - 3D Photo Conversion - Cinematic Control - Precise orbital/pan/zoom control - Corrects geometric artifacts

and Wan2.2 again. MoCam - novel view synthesis. Lets you change the camera trajectory of a photo/video. - Video Re-camera - 3D Photo Conversion - Cinematic Control - Precise orbital/pan/zoom control - Corrects geometric artifacts

Wildminder

14,069 Aufrufe • vor 2 Monaten

🕹️We are excited to introduce "ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation" ChronoEdit reframes image editing as a video generation task to encourage temporal consistency. It leverages a temporal reasoning stage that denoises with “video reasoning tokens” to "reason" on physically plausible edits. See the attached video for results. Project Page: Arxiv: Code and model are coming.

🕹️We are excited to introduce "ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation" ChronoEdit reframes image editing as a video generation task to encourage temporal consistency. It leverages a temporal reasoning stage that denoises with “video reasoning tokens” to "reason" on physically plausible edits. See the attached video for results. Project Page: Arxiv: Code and model are coming.

Huan Ling

36,869 Aufrufe • vor 9 Monaten

🚀 Excited to announce the open-sourcing of Hallo3, a new avatar generative model! 🎨✨ Hallo3 is capable of creating highly realistic avatars with dynamic backgrounds, expressive foregrounds, and various head orientations. Perfect for bringing your ideas to life! 🛠️ Try it out and let us know your feedback or suggestions. Github 👉 Project page:

Siyu ZHU

35,424 Aufrufe • vor 1 Jahr

Novel view synthesis has long been a core challenge in 3D vision. But how much 3D inductive bias is truly needed? —Surprisingly, very little! Introducing "LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias"—a fully transformer-based approach that enables scalable, generalizable, and fully data-driven novel view synthesis, from sparse posed inputs. 🧵(1/6) Project Page:

Novel view synthesis has long been a core challenge in 3D vision. But how much 3D inductive bias is truly needed? —Surprisingly, very little! Introducing "LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias"—a fully transformer-based approach that enables scalable, generalizable, and fully data-driven novel view synthesis, from sparse posed inputs. 🧵(1/6) Project Page:

Haian Jin

114,757 Aufrufe • vor 1 Jahr

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

Sherwin Bahmani

66,563 Aufrufe • vor 9 Monaten

ScenarioControl 🚗🛣️ - Scenario Generation from a single Dashcam Image 📸 or Text Prompt 💬!! Excited to introduce a new vision-language control mechanism for learned driving scenario generation. Given a single dashcam image or a scene prompt or an image, we generate a full scene layout 🧩, temporally consistent rollouts, including map 🗺️, agents 🚗, and ego video🛣️ ScenarioControl enables direct, fine-grained control over layout and traffic while preserving realism. It operates in a vectorized latent space with a new cross-global control mechanism to fuse vision-language inputs with scene structure while preserving realism. Interfaces seamlessly with generative video models! Project: Super fun project by Lili Gao, Yanbo Xu , William Koch, Samuele Ruffino, Luke Rowe , Behdad Chalaki, Dmitriy Rivkin, Julian Ost, Roger Girgis, Mario Bijelic.

ScenarioControl 🚗🛣️ - Scenario Generation from a single Dashcam Image 📸 or Text Prompt 💬!! Excited to introduce a new vision-language control mechanism for learned driving scenario generation. Given a single dashcam image or a scene prompt or an image, we generate a full scene layout 🧩, temporally consistent rollouts, including map 🗺️, agents 🚗, and ego video🛣️ ScenarioControl enables direct, fine-grained control over layout and traffic while preserving realism. It operates in a vectorized latent space with a new cross-global control mechanism to fuse vision-language inputs with scene structure while preserving realism. Interfaces seamlessly with generative video models! Project: Super fun project by Lili Gao, Yanbo Xu , William Koch, Samuele Ruffino, Luke Rowe , Behdad Chalaki, Dmitriy Rivkin, Julian Ost, Roger Girgis, Mario Bijelic.

Felix Heide

22,284 Aufrufe • vor 2 Monaten

Stability AI just dropped Stable Virtual Camera on Hugging Face a generalist diffusion model designed to address the exciting challenge of Novel View Synthesis (NVS). With just one or a few images, it allows you to create a smooth trajectory video from any viewpoint you desire.

Stability AI just dropped Stable Virtual Camera on Hugging Face a generalist diffusion model designed to address the exciting challenge of Novel View Synthesis (NVS). With just one or a few images, it allows you to create a smooth trajectory video from any viewpoint you desire.

AK

133,583 Aufrufe • vor 1 Jahr

We are excited to share EMDB, a novel dataset of 3D human poses for in-the-wild monocular videos, including global trajectories. Data and toolkit code is now available. More details in the thread below. Project Page:

We are excited to share EMDB, a novel dataset of 3D human poses for in-the-wild monocular videos, including global trajectories. Data and toolkit code is now available. More details in the thread below. Project Page:

AIT Lab

14,601 Aufrufe • vor 2 Jahren

📢📢📢 𝐀𝐂𝟑𝐃: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers TL;DR: for 3D camera control in generative video, it really helps knowing *which* part of your model you should mess with Internship by Sherwin Bahmani at Snap

📢📢📢 𝐀𝐂𝟑𝐃: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers TL;DR: for 3D camera control in generative video, it really helps knowing which part of your model you should mess with Internship by Sherwin Bahmani at Snap

Andrea Tagliasacchi 🇨🇦→✈️→🇺🇲 (@CVPR)

23,040 Aufrufe • vor 1 Jahr

Excited to share our recent work, UniSH, which unifies dynamic 3D scene reconstruction and SMPL estimation within a single framework. (Left-top is input video). Code has been released! Project page: Paper:

Excited to share our recent work, UniSH, which unifies dynamic 3D scene reconstruction and SMPL estimation within a single framework. (Left-top is input video). Code has been released! Project page: Paper:

Yuan Liu

19,242 Aufrufe • vor 6 Monaten

After a year of team work, we're thrilled to introduce Depth Anything 3 (DA3)! 🚀 Aiming for human-like spatial perception, DA3 extends monocular depth estimation to any-view scenarios, including single images, multi-view images, and video. In pursuit of minimal modeling, DA3 reveals two key insights: 💎 A plain transformer (e.g., vanilla DINO) is enough. No specialized architecture. ✨ A single depth-ray representation is enough. No complex 3D tasks. Three series of models have been released: the main DA3 series, a monocular metric estimation series, and a monocular depth estimation series. The core team members, aside from me: Haotong Lin, Sili Chen, Jun Hao Liew, Donny Y. Chen. 👇(1/n) #DepthAnything3

After a year of team work, we're thrilled to introduce Depth Anything 3 (DA3)! 🚀 Aiming for human-like spatial perception, DA3 extends monocular depth estimation to any-view scenarios, including single images, multi-view images, and video. In pursuit of minimal modeling, DA3 reveals two key insights: 💎 A plain transformer (e.g., vanilla DINO) is enough. No specialized architecture. ✨ A single depth-ray representation is enough. No complex 3D tasks. Three series of models have been released: the main DA3 series, a monocular metric estimation series, and a monocular depth estimation series. The core team members, aside from me: Haotong Lin, Sili Chen, Jun Hao Liew, Donny Y. Chen. 👇(1/n) #DepthAnything3

Bingyi Kang

514,660 Aufrufe • vor 8 Monaten

🚀 Introducing InterDyn — our newly accepted CVPR work that explores controllable synthesis of interactive dynamics! Building upon powerful video diffusion models, InterDyn infers future motion and interactions directly from an input image and a dynamic control signal (e.g., a moving hand mask). Check out how we push the boundaries of intuitive physics with video generative models. Project page: Arxiv: #GenAI #AIGC #VideoGen #ML #ComputerVision #CVPR2025 🧵1/6

🚀 Introducing InterDyn — our newly accepted CVPR work that explores controllable synthesis of interactive dynamics! Building upon powerful video diffusion models, InterDyn infers future motion and interactions directly from an input image and a dynamic control signal (e.g., a moving hand mask). Check out how we push the boundaries of intuitive physics with video generative models. Project page: Arxiv: #GenAI #AIGC #VideoGen #ML #ComputerVision #CVPR2025 🧵1/6

Haven (Haiwen) Feng

44,898 Aufrufe • vor 1 Jahr