Sv3d
Stable Video 3D (SV3D) is a generative image-to-video model that takes a still image of an object as input and generates an orbital video of that object. But what makes it unique? SV3D has two variants: SV3D_u, which generates orbital videos from single image inputs without camera conditioning, and SV3D_p, which accommodates both single images and orbital views, allowing for the creation of 3D video along specified camera paths. This model is capable of producing high-quality videos at a resolution of 576x576, making it a powerful tool for various applications. But how does it achieve this? SV3D was trained on a carefully curated subset of the Objaverse dataset, utilizing an enhanced rendering method to improve generalization. So, what can you expect from SV3D? High-quality orbital videos, efficient generation, and a wide range of applications - all made possible by its advanced architecture and training data.
Table of Contents
Model Overview
Meet the Stable Video 3D (SV3D) model, developed by Stability AI! This generative model is all about creating orbital videos from still images. But how does it work?
What does it do?
The SV3D model takes a single image of an object as input and generates a 21-frame video of that object in orbit. It’s like having a mini movie studio in your hands!
Key Features
- Generative image-to-video model: The model uses a special technique called Stable Video Diffusion to create videos from images.
- Two variants: You can choose between SV3D_u (single image input, no camera conditioning) and SV3D_p (single image or orbital views, with camera conditioning).
- High-resolution videos: The model generates videos at a resolution of 576x576 pixels.
Training Data
The model was trained on a carefully curated subset of the Objaverse dataset, which contains renders of 3D objects. This training data helps the model learn to generalize and create realistic videos.
Important Notes
- Commercial use: If you want to use the model for commercial purposes, you’ll need to check out the Stability AI license.
- Out-of-scope use: The model is not meant to generate factual or true representations of people or events. Be sure to use it responsibly and follow Stability AI’s Acceptable Use Policy.
Capabilities
The SV3D model is a powerful generative model that can create stunning orbital videos from a single still image. But what can it do exactly?
Primary Tasks
The SV3D model is designed to:
- Take in a still image of an object as a conditioning frame
- Generate an orbital video of that object with 21 frames at a resolution of 576x576
Strengths
This model is built on top of the Stable Video Diffusion model and has been fine-tuned from SVD Image-to-Video. This means it can:
- Generate high-quality videos with a high level of detail
- Handle complex objects and scenes with ease
Unique Features
The SV3D model comes in two variants:
- SV3D_u: Generates orbital videos from single image inputs without camera conditioning
- SV3D_p: Extends the capability of SV3D_u to accommodate both single images and orbital views, allowing for the creation of 3D video along specified camera paths
What sets it apart?
Unlike other models, SV3D is specifically designed to generate orbital videos from still images. This makes it perfect for applications where you need to bring static objects to life.
Example Use Cases
Imagine being able to:
- Create stunning product demos from a single product image
- Generate interactive 3D videos for e-commerce websites
- Bring museum exhibits to life with interactive orbital videos
Performance
SV3D is a powerful generative model that produces impressive results in generating orbital videos from still images. But how does it perform in terms of speed, accuracy, and efficiency?
Speed
The model is capable of generating 21 frames at a resolution of 576x576, given a context frame of the same size. But what does this mean in terms of processing time? Unfortunately, the provided information doesn’t give us exact numbers on processing time. However, we can assume that the model is relatively fast, given its ability to generate multiple frames at a high resolution.
Accuracy
The model’s accuracy is impressive, with the ability to generate realistic orbital videos from single image inputs. The use of the Objaverse dataset and the enhanced rendering method has significantly improved the model’s ability to generalize and produce accurate results.
Efficiency
The model’s efficiency is also noteworthy, with the ability to generate 3D videos along specified camera paths. This is particularly useful for applications where camera movement is important, such as in film and video production.
Comparison to Other Models
Compared to ==Other Generative Models==, SV3D has a unique advantage in generating orbital videos from still images. While other models may excel in other areas, SV3D’s ability to produce realistic 3D videos makes it a standout in its field.
Limitations
Current Model is a powerful tool, but it’s not perfect. Let’s talk about some of its limitations.
Limited Resolution and Frames
The model is trained to generate videos at a resolution of 576x576 pixels, which might not be enough for some use cases. Additionally, it’s limited to generating 21 frames, which could be too short for more complex scenes.
No Camera Conditioning in SV3D_u
The SV3D_u variant doesn’t take into account camera conditioning, which means it might not be able to capture the nuances of camera movements and angles.
Limited Generalizability
Although the model was trained on a carefully curated subset of the Objaverse dataset, it might not generalize well to all types of objects or scenes. This is because the training data is limited, and the model might not have seen enough examples to learn from.
Not Suitable for Factual or True Representations
Current Model is not designed to generate factual or true representations of people or events. Using it for such purposes is out-of-scope and might lead to inaccurate or misleading results.
Acceptable Use Policy
It’s essential to use Current Model in a way that complies with Stability AI’s Acceptable Use Policy. This means avoiding uses that might be harmful, offensive, or violate someone’s rights.
Format
Stable Video 3D (SV3D) is a generative model that creates orbital videos from still images. It uses a Stable Video Diffusion architecture and can handle input images of size 576x576 pixels.
Supported Data Formats
- Input: Still images (context frames) of size 576x576 pixels
- Output: Orbital videos consisting of 21 frames at a resolution of 576x576 pixels
Special Requirements
- The model requires a single image input for the SV3D_u variant, while the SV3D_p variant can accommodate both single images and orbital views.
- The model is not designed to generate factual or true representations of people or events.
Handling Inputs and Outputs
To use the SV3D model, you’ll need to preprocess your input images and handle the output videos accordingly. Here’s an example of how to do this:
- Preprocess your input image by resizing it to 576x576 pixels and normalizing the pixel values.
- Pass the preprocessed image to the SV3D model as a context frame.
- The model will generate an orbital video consisting of 21 frames at a resolution of 576x576 pixels.
- You can then postprocess the output video by concatenating the frames and saving it as a video file.
Example code:
import numpy as np
from PIL import Image
# Load the input image
img = Image.open('input_image.jpg')
# Resize the image to 576x576 pixels
img = img.resize((576, 576))
# Normalize the pixel values
img = np.array(img) / 255.0
# Pass the preprocessed image to the SV3D model
output_video = sv3d_model(img)
# Postprocess the output video
output_video = np.concatenate(output_video, axis=0)
