Destination-Conditioned Adaptation with Semantic-Geometric Alignment for Generative Inbetweening
We propose DSG-GI, a framework that adapts pretrained latent video diffusion models through Destination-Conditioned Adaptation and Semantic-Geometric Alignment.
ProofPaper ↗
Key points
- Generative inbetweening aims to synthesize temporally coherent intermediate frames between a start frame and a target frame.
- Although pretrained image-to-video diffusion models provide strong priors for large-motion inbetweening, they are not explicitly designed to reach a specified target frame.
- Destination-Conditioned Adaptation treats the target frame as the destination of the sequence and provides temporally progressive guidance along the generated trajectory.
- Semantic-Geometric Alignment aligns the token-wise spatio-temporal relational geometry of backbone features with that of a frozen video foundation encoder and applies a flow-matching-aware latent flow constraint to samples near the data endpoint of the probability path.
Sources (1)
- [1]Destination-Conditioned Adaptation with Semantic-Geometric Alignment for Generative InbetweeningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 08:10 AM
We propose DSG-GI, a framework that adapts pretrained latent video diffusion models through Destination-Conditioned Adaptation and Semantic-Geometric Alignment.
Generative inbetweening aims to synthesize temporally coherent intermediate frames between a start frame and a target frame.
Extractive summary: sentences quoted from the sources.
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