Stable Part Diffusion 4D (SP4D)
is a generative model based on
Stable Video 4D 2.0 (SV4D 2.0)
, a video-to-4D diffusion model for multi-view part video synthesis and animatable 3D asset generation.
Please note: For individuals or organizations generating annual revenue of US $1,000,000 (or local currency equivalent) or more, regardless of the source of that revenue, you must obtain an enterprise commercial license directly from Stability AI before commercially using SP4D, or any derivative work of SP4D or its outputs, such as “fine tune” or “low-rank adaption” models. You may submit a request for an Enterprise License at
https://stability.ai/enterprise
. Please refer to Stability AI’s Community License, available at
https://stability.ai/license
, for more information.
Model details
: This model is trained to generate 48 RGB frames and part segmentation maps (4 video frames x 12 camera views) at 576x576 resolution, given a 4-frame input video of the same size. Based on our previous 4D model
SV4D 2.0
,
SP4D
can simultaneously generate multi-view RGB videos as well as the corresponding kinematic part segmentations that are consistent across time and camera views. The generated part videos can then be used to create animation-ready 3D assets with part-aware rigging capabilities. Please check our
arxiv paper
and
video summary
for details.
License
Community License:
Free for research, non-commercial, and commercial use by organizations and individuals generating annual revenue of US $1,000,000 (or local currency equivalent) or less, regardless of the source of that revenue. If your annual revenue exceeds US $1M, any commercial use of this model or derivative works thereof requires obtaining an Enterprise License directly from Stability AI. You may submit a request for an Enterprise License at
https://stability.ai/enterprise
. Please refer to Stability AI’s Community License, available at
https://stability.ai/license
, for more information.
We use renders from the
Objaverse-XL
dataset, available under the Open Data Commons Attribution License, utilizing our enhanced rendering method that more closely replicates the distribution of images found in the real world, significantly improving our model's ability to generalize. We filter objects based on the review of licenses and curated a subset suitable for our training needs.
The model was not trained to be factual or true representations of people or events. As such, using the model to generate such content is out-of-scope of the abilities of this model.
Safety
As part of our safety-by-design and responsible AI deployment approach, we implement safety measures throughout the development of our models, from the time we begin pre-training a model to the ongoing development, fine-tuning, and deployment of each model. We have implemented a number of safety mitigations that are intended to reduce the risk of severe harms. However, we recommend that developers conduct their own testing and apply additional mitigations based on their specific use cases.
For more about our approach to Safety, please visit our
Safety page
.
Contact
Please report any issues with the model or contact us:
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