The authors curated 35,800 video editing pairs from three complementary sources: 3D-rendered scenes, model-generated videos, and real-world footage, combined with general insertion pairs from the ROSE dataset.
ALIVE inserts objects that respond coherently to video actions
A diffusion editor trained on 35,800 curated editing pairs, with optional vision-language-model guidance, improves interaction fidelity by 43.9% over prior baselines.
Big Tech
Zhenghong Zhou · Zhe Lin · Jiebo Luo · Yuqian Zhou
University of Rochester · Adobe Research
Research Digest··1 min read
The authors introduce ALIVE, a framework for inserting objects into videos so that they participate in ongoing interactions, such as being lifted or cut.
Why this paper
From Adobe Research and University of Rochester
In one line
ALIVE inserts objects into videos that coherently interact with the source actions using an edited first frame and object-only instruction.
What we could check
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- ·No weights link found
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- ·No stated limitations found
- ✓Reports numbers on named benchmarks (2 benchmarks)
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