EA-WM generates more accurate robot world rollouts by projecting actions as structured visual fields in camera space and using event-aware bidirectional fusion to better capture interaction dynamics.
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Lumiere: A space-time diffusion model for video generation
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representative citing papers
Shortcut models enable high-quality single or few-step sampling in diffusion models with one network and training phase by conditioning on desired step size.
A diffusion model trained on DOOM play sessions generates stable real-time interactive game frames at 20 FPS with quality near lossy JPEG.
OmniManim improves render quality in educational animation code generation by using a Vision Agent with coarse-to-fine bounding-box denoising and interpolation-aware optimization on new datasets.
A zero-shot subject-driven video generation framework that decomposes the task into identity injection from 200K subject-image pairs and motion preservation from 4K arbitrary videos, trained in 288 A100 GPU hours on CogVideoX-5B to match prior performance at 1% compute.
DOLLAR combines variational score and consistency distillation for few-step video generation plus latent reward optimization, reporting 82.57 VBench score and up to 278x speedup over the teacher diffusion model for 128-frame 10-second videos.
GenFocal uses probabilistic ML to downscale coarse climate projections to fine-scale weather events without paired training data and samples rare high-impact events more accurately than prior methods.
A single transformer combines language modeling loss and diffusion loss on mixed-modality data, scaling to 7B parameters and 2T tokens while matching specialized language and diffusion models.
VideoPhy benchmark shows state-of-the-art text-to-video models follow physical commonsense and text prompts in only 39.6% of cases for the best model.
CameraCtrl enables accurate camera pose control in video diffusion models through a trained plug-and-play module and dataset choices emphasizing diverse camera trajectories with matching appearance.
VideoPoet is a large language model that performs zero-shot video generation with audio from diverse multimodal conditioning signals.
A 30B-parameter transformer and related models generate high-quality videos and audio, claiming state-of-the-art results on text-to-video, video editing, personalization, and audio generation tasks.
The paper reviews the background, technology, applications, limitations, and future directions of OpenAI's Sora text-to-video generative model based on public information.
citing papers explorer
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EA-WM: Event-Aware Generative World Model with Structured Kinematic-to-Visual Action Fields
EA-WM generates more accurate robot world rollouts by projecting actions as structured visual fields in camera space and using event-aware bidirectional fusion to better capture interaction dynamics.
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One Step Diffusion via Shortcut Models
Shortcut models enable high-quality single or few-step sampling in diffusion models with one network and training phase by conditioning on desired step size.
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Diffusion Models Are Real-Time Game Engines
A diffusion model trained on DOOM play sessions generates stable real-time interactive game frames at 20 FPS with quality near lossy JPEG.
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See Before You Code: Learning Visual Priors for Spatially Aware Educational Animation Generation
OmniManim improves render quality in educational animation code generation by using a Vision Agent with coarse-to-fine bounding-box denoising and interpolation-aware optimization on new datasets.
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Learning Zero-Shot Subject-Driven Video Generation Using 1% Compute
A zero-shot subject-driven video generation framework that decomposes the task into identity injection from 200K subject-image pairs and motion preservation from 4K arbitrary videos, trained in 288 A100 GPU hours on CogVideoX-5B to match prior performance at 1% compute.
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DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization
DOLLAR combines variational score and consistency distillation for few-step video generation plus latent reward optimization, reporting 82.57 VBench score and up to 278x speedup over the teacher diffusion model for 128-frame 10-second videos.
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Regional climate risk assessment from climate models using probabilistic machine learning
GenFocal uses probabilistic ML to downscale coarse climate projections to fine-scale weather events without paired training data and samples rare high-impact events more accurately than prior methods.
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Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
A single transformer combines language modeling loss and diffusion loss on mixed-modality data, scaling to 7B parameters and 2T tokens while matching specialized language and diffusion models.
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VideoPhy: Evaluating Physical Commonsense for Video Generation
VideoPhy benchmark shows state-of-the-art text-to-video models follow physical commonsense and text prompts in only 39.6% of cases for the best model.
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CameraCtrl: Enabling Camera Control for Text-to-Video Generation
CameraCtrl enables accurate camera pose control in video diffusion models through a trained plug-and-play module and dataset choices emphasizing diverse camera trajectories with matching appearance.
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VideoPoet: A Large Language Model for Zero-Shot Video Generation
VideoPoet is a large language model that performs zero-shot video generation with audio from diverse multimodal conditioning signals.
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Movie Gen: A Cast of Media Foundation Models
A 30B-parameter transformer and related models generate high-quality videos and audio, claiming state-of-the-art results on text-to-video, video editing, personalization, and audio generation tasks.
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Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
The paper reviews the background, technology, applications, limitations, and future directions of OpenAI's Sora text-to-video generative model based on public information.
- CityRAG: Stepping Into a City via Spatially-Grounded Video Generation