arXiv:2512.05672v2 Announce Type: replace-cross Abstract: Recent approaches in controllable novel view video generation often rely on fine-tuning pre-trained Video Diffusion Models (VDMs). This dominant paradigm is computationally expensive and frequently suffers from catastrophic forgetting of the model's original generative priors. To address this challenge, here we propose InverseCrafter, a VDM training-free framework that reformulates novel view video generation as an inpainting-based inverse problem in the latent space, eliminating the need for any annotated 4D training data. The core of
The proliferation of video generation and 3D reconstruction technologies necessitates more efficient and less resource-intensive methods for novel view synthesis.
This development could significantly reduce the computational burden and data requirements for creating realistic, controllable videos and 3D content, democratizing access to and accelerating progress in advanced AI applications.
Novel view video generation shifts from a costly fine-tuning paradigm to a more efficient inverse problem approach, eliminating the need for extensive 4D training data and VDM retraining.
- · AI developers
- · Content creators
- · Gaming industry
- · Metaverse platforms
- · Traditional high-compute video generative models
- · Companies reliant on large 4D datasets
Reduced costs and increased accessibility for generating high-quality synthetic video and 3D content.
Faster iteration cycles for AI research and development in video synthesis and 3D reconstruction.
Potential for new applications in personalized content, virtual reality, and digital twins due to lowered barriers to creation.
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Read at arXiv cs.AI