PerpetualWonder introduces a closed-loop generative simulator with a unified physical-visual representation for long-horizon action-conditioned 4D scene generation from one image.
Step-video-ti2v technical re- port: A state-of-the-art text-driven image-to-video genera- tion model
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
VideoASMR-Bench shows state-of-the-art VLMs fail to reliably detect AI-generated ASMR videos from real ones, though humans can still identify the fakes relatively easily.
CineCap combines structured reasoning and RL rewards to outperform baselines on cinematographic video captioning using a new 472-pair benchmark.
PARE applies structure-aware head pruning and timestep/content-conditioned block routing to compress video DiTs, reducing per-step compute while preserving quality on Wan2.1-14B.
citing papers explorer
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PerpetualWonder: Long-Horizon Action-Conditioned 4D Scene Generation
PerpetualWonder introduces a closed-loop generative simulator with a unified physical-visual representation for long-horizon action-conditioned 4D scene generation from one image.
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VideoASMR-Bench: Can AI-Generated ASMR Videos Fool VLMs and Humans?
VideoASMR-Bench shows state-of-the-art VLMs fail to reliably detect AI-generated ASMR videos from real ones, though humans can still identify the fakes relatively easily.
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CineCap: Structured Reasoning with Spatio-Temporal Anchors for Cinematographic Video Captioning
CineCap combines structured reasoning and RL rewards to outperform baselines on cinematographic video captioning using a new 472-pair benchmark.
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PARE: Pruning and Adaptive Routing for Efficient Video Generation
PARE applies structure-aware head pruning and timestep/content-conditioned block routing to compress video DiTs, reducing per-step compute while preserving quality on Wan2.1-14B.