Echo-Forcing decouples stable anchors, compressed history, and recent dynamics in video diffusion KV caches using hierarchical memory, scene recall frames, and difference-aware decay to support interactive long video generation under bounded cache.
Lumiere: A space-time diffusion model for video generation
7 Pith papers cite this work. Polarity classification is still indexing.
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CaC presents a new spatiotemporal concentrating reward model for video anomalies, built on a novel large-scale dataset and three-stage training with RL and IoU rewards, claiming 25.7% accuracy gains and 11.7% anomaly reduction.
Immune2V immunizes images against dual-stream I2V generation by enforcing temporally balanced latent divergence and aligning generative features to a precomputed collapse trajectory, yielding stronger persistent degradation than image-level baselines.
Fine-tuning a video generator on a new 17K reasoning-video dataset improves Chain-of-Frame reasoning, and adding learnable visual/textual reasoning tokens yields further gains on external benchmarks.
LingBot-Video is an open-source MoE video foundation model for embodied intelligence that scales to 120B parameters, integrates robot data, and uses multi-dimensional RL to improve physical plausibility.
Flow matching generative models preserve sample quality, diversity, and latent representations despite pruning 50% of the CelebA-HQ dataset or altering architecture and training configurations.
VDCook is an automated, self-evolving platform for generating in-domain video datasets for MLLMs via natural language queries, retrieval-synthesis, and multi-dimensional metadata.
citing papers explorer
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Echo-Forcing: A Scene Memory Framework for Interactive Long Video Generation
Echo-Forcing decouples stable anchors, compressed history, and recent dynamics in video diffusion KV caches using hierarchical memory, scene recall frames, and difference-aware decay to support interactive long video generation under bounded cache.
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CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating
CaC presents a new spatiotemporal concentrating reward model for video anomalies, built on a novel large-scale dataset and three-stage training with RL and IoU rewards, claiming 25.7% accuracy gains and 11.7% anomaly reduction.
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Immune2V: Image Immunization Against Dual-Stream Image-to-Video Generation
Immune2V immunizes images against dual-stream I2V generation by enforcing temporally balanced latent divergence and aligning generative features to a precomputed collapse trajectory, yielding stronger persistent degradation than image-level baselines.
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OpenCoF: Learning to Reason Through Video Generation
Fine-tuning a video generator on a new 17K reasoning-video dataset improves Chain-of-Frame reasoning, and adding learnable visual/textual reasoning tokens yields further gains on external benchmarks.
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Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence
LingBot-Video is an open-source MoE video foundation model for embodied intelligence that scales to 120B parameters, integrates robot data, and uses multi-dimensional RL to improve physical plausibility.
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The Amazing Stability of Flow Matching
Flow matching generative models preserve sample quality, diversity, and latent representations despite pruning 50% of the CelebA-HQ dataset or altering architecture and training configurations.
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VDCook:DIY video data cook your MLLMs
VDCook is an automated, self-evolving platform for generating in-domain video datasets for MLLMs via natural language queries, retrieval-synthesis, and multi-dimensional metadata.