arXiv:2601.23286v4 Announce Type: replace-cross Abstract: While recent video diffusion models (VDMs) produce visually impressive results, they fundamentally struggle to maintain 3D structural consistency, often resulting in object deformation or spatial drift. We hypothesize that these failures arise because standard denoising objectives lack explicit incentives for geometric coherence. To address this, we introduce VideoGPA (Video Geometric Preference Alignment), a data-efficient self-supervised framework that leverages a geometry foundation model to automatically derive dense preference sign
The rapid advancement in video diffusion models has highlighted their limitations in geometric consistency, prompting immediate research into solutions for more realistic and stable 3D video generation.
Achieving 3D-consistent video generation is crucial for developing high-fidelity AI-generated content, virtual reality, and robotics, enabling more immersive and functional applications.
This research introduces a novel self-supervised framework to address a fundamental limitation in current video diffusion models, promising more geometrically accurate and stable outputs.
- · AI content creators
- · Metaverse developers
- · Computer vision researchers
- · Generative AI platforms
- · Creators reliant on manual 3D animation
- · Models lacking geometric understanding
AI-generated videos will exhibit significantly improved 3D consistency, reducing artifacts and increasing realism.
This improvement in visual fidelity could accelerate the adoption of AI-generated content across industries, from entertainment to product design.
The enhanced realism might blur the lines between real and AI-generated video, necessitating advanced detection methods and ethical guidelines for synthetic media.
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Read at arXiv cs.LG