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Introducing Stable Virtual Camera: This multi-view diffusion model transforms 2D images into immersive 3D videos with realistic depth and perspective—without complex reconstruction or scene-specific optimization.

Stability AI

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197,203 görüntüleme • 1 yıl önce •via X (Twitter)

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Stability AI profil fotoğrafı

Stability AI1 yıl önce

A virtual camera is a digital tool used in filmmaking and 3D animation to capture and navigate digital scenes in real-time. Stable Virtual Camera builds upon this concept, combining the familiar control of traditional virtual cameras with the power of generative AI to offer precise, intuitive control over 3D video outputs.

Stability AI profil fotoğrafı

Stability AI1 yıl önce

Key Capabilities: ✅ Dynamic Camera Control: Supports custom & preset paths (360°, Lemniscate, Dolly Zoom, Spiral, Pan, Move & more). ✅ Flexible Inputs: Generates 3D videos from just one input image or up to 32. ✅ Multiple Aspect Ratios: Produces square, portrait, landscape & custom aspect ratios, with no additional training required. ✅ Long Video Generation: Maintains 3D consistency for up to 1,000 frames.

Stability AI profil fotoğrafı

Stability AI1 yıl önce

Stable Virtual Camera achieves state-of-the-art results in novel view synthesis (NVS) benchmarks, outperforming models like ViewCrafter and CAT3D. It excels in both large-viewpoint NVS, which emphasizes generation capacity, and small-viewpoint NVS, which prioritizes temporal smoothness.

Stability AI profil fotoğrafı

Stability AI1 yıl önce

Currently in research preview, the model is available for use under a Non-Commercial License. You can read the paper, download the weights on @HuggingFace, and access the code on @GitHub now. Learn more here:

PowerBeatsVR profil fotoğrafı

PowerBeatsVR3 yıl önce

Get ready for a full-body VR workout that’s fun, fast, and intuitive — Play PowerBeatsVR (Now on Meta Quest) 🔥

Ben Pielstick profil fotoğrafı

Ben Pielstick1 yıl önce

Could you use this as synthetic photogrammetry to generate a 3d object and export it as an FBX.

JaimeOrtega.eth profil fotoğrafı

JaimeOrtega.eth1 yıl önce

Nice

Richard Entrup profil fotoğrafı

Richard Entrup1 yıl önce

👀

TommyO profil fotoğrafı

TommyO1 yıl önce

It understood and recreated the reflection in the mirror? That’s amazing!

Jikku Jose profil fotoğrafı

Jikku Jose1 yıl önce

Thats a lame video for such a nice product.

mat CLONE |Voltix.ai 👾 , ၊||၊ DeSpeed ၊||၊ profil fotoğrafı

mat CLONE |Voltix.ai 👾 , ၊||၊ DeSpeed ၊||၊1 yıl önce

Wow, a Stable Virtual Camera? Talk about taking 2D to 3D in style! PublicAI is all about transforming data too—let's make those pixels dance! 🎥✨

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📢Introducing 360Anything, our method for lifting any perspective image or video to gravity-aligned 360° panoramas without using any camera or 3D information. This enables consistent novel view synthesis and 3D scene reconstruction. Project page: 🧵

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🔥 Introducing MVLift: Generate realistic 3D motion without any 3D training data - just using 2D poses from monocular videos! Applicable to human motion, human-object interaction & animal motion. Joint work w/ Jiajun Wu & Karen 💡 How? We reformulate 3D motion estimation as generating consistent multi-view 2D pose sequences. Our framework uses 2D motion diffusion to progressively establish multi-view consistency, requiring only single-view 2D pose sequences for training. Project: Video with demonstration: Paper:

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MVDream: Multi-view Diffusion for 3D Generation paper page: propose MVDream, a multi-view diffusion model that is able to generate geometrically consistent multi-view images from a given text prompt. By leveraging image diffusion models pre-trained on large-scale web datasets and a multi-view dataset rendered from 3D assets, the resulting multi-view diffusion model can achieve both the generalizability of 2D diffusion and the consistency of 3D data. Such a model can thus be applied as a multi-view prior for 3D generation via Score Distillation Sampling, where it greatly improves the stability of existing 2D-lifting methods by solving the 3D consistency problem. Finally, we show that the multi-view diffusion model can also be fine-tuned under a few shot setting for personalized 3D generation, i.e. DreamBooth3D application, where the consistency can be maintained after learning the subject identity.

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Introducing VGGT (CVPR'25), a feedforward Transformer that directly infers all key 3D attributes from one, a few, or hundreds of images, in seconds! No expensive optimization needed, yet delivers SOTA results for: ✅ Camera Pose Estimation ✅ Multi-view Depth Estimation ✅ Dense Point Cloud Reconstruction ✅ Point Tracking Project Page: Code & Weights:

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🚀Introducing LLaVA-NeXT Interleave: Now AI can understand and reason with multiple images at once - This opens up multi-image scenarios like multi-frame videos, multi-view 3D, and multiple inter-leaved images. - An all round LMM that can understand videos, images, and 3D More⬇️

🚀Introducing LLaVA-NeXT Interleave: Now AI can understand and reason with multiple images at once - This opens up multi-image scenarios like multi-frame videos, multi-view 3D, and multiple inter-leaved images. - An all round LMM that can understand videos, images, and 3D More⬇️

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Multi-view Reconstruction via SfM-guided Monocular Depth Estimation Contributions: • We propose a novel approach to inject SfM priors into diffusion-based depth estimation, enabling highly accurate and multi-view consistent depth predictions for each viewpoint. • Based on the proposed depth estimator, we design a new multi-view 3D geometry reconstruction framework and process some synthetic datasets to facilitate training. • We evaluate our method on diverse real-world scene data, including objects, indoor environments, streetscapes, and aerial scenes, demonstrating the superior performance and generalization capability of our approach.

Multi-view Reconstruction via SfM-guided Monocular Depth Estimation Contributions: • We propose a novel approach to inject SfM priors into diffusion-based depth estimation, enabling highly accurate and multi-view consistent depth predictions for each viewpoint. • Based on the proposed depth estimator, we design a new multi-view 3D geometry reconstruction framework and process some synthetic datasets to facilitate training. • We evaluate our method on diverse real-world scene data, including objects, indoor environments, streetscapes, and aerial scenes, demonstrating the superior performance and generalization capability of our approach.

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DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.

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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

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