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New framework improves generative inbetweening with destination guidance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DSG-GI, a new framework designed to improve generative inbetweening, a process that synthesizes intermediate frames between a start and target frame. This method adapts existing image-to-video diffusion models by incorporating Destination-Conditioned Adaptation and Semantic-Geometric Alignment. These techniques enhance the model's ability to adhere to a specified target frame and maintain stable, coherent motion throughout the generated sequence. Experiments show that DSG-GI leads to better temporal stability, faithfulness to the target frame, and overall perceptual quality. AI

IMPACT Enhances video generation quality and target frame adherence, potentially improving applications in animation and video editing.

RANK_REASON Academic paper detailing a new method for generative inbetweening. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework improves generative inbetweening with destination guidance

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Academic paper detailing a new method for generative inbetweening. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Zhiyu Xu (Tianjin University), Wei Shang (Singapore Management University), Yushen Xu (Tianjin University), Wenwen Li (Tianjin Medical University), Zhiyan Xu (Southeast University), Pengfei Zhu (Tianjin University), Dongwei Ren (Tianjin University) ·

    Destination-Conditioned Adaptation with Semantic-Geometric Alignment for Generative Inbetweening

    arXiv:2610.13187v1 Announce Type: new Abstract: 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, th…

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