✨YingVideo-MV: Music-Driven Multi-Stage Video Generation

📝 Summary:
YingVideo-MV is the first framework to generate high-quality, music-driven long performance videos with synchronized camera motion. It uses audio analysis, diffusion transformers, and a camera adapter, achieving precise music-motion-camera synchronization.

🔹 Publication Date: Published on Dec 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.02492
• PDF: https://arxiv.org/pdf/2512.02492

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#VideoGeneration #MusicAI #GenerativeAI #DiffusionModels #ComputerVision

✨BlockVid: Block Diffusion for High-Quality and Consistent Minute-Long Video Generation

📝 Summary:
BlockVid introduces a block diffusion framework for high-quality, coherent minute-long video generation. It overcomes error accumulation via a semantic-aware sparse KV cache, Block Forcing training, and dedicated noise scheduling. BlockVid outperforms existing methods and proposes LV-Bench, a new...

🔹 Publication Date: Published on Nov 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22973
• PDF: https://arxiv.org/pdf/2511.22973
• Project Page: https://ziplab.co/BlockVid/
• Github: https://github.com/alibaba-damo-academy/Inferix/

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#VideoGeneration #DiffusionModels #GenerativeAI #DeepLearning #ComputerVision

✨MultiShotMaster: A Controllable Multi-Shot Video Generation Framework

📝 Summary:
MultiShotMaster is a framework for controllable multi-shot video generation. It extends a single-shot model with novel RoPE variants for flexible shot arrangement, narrative order, and spatiotemporal reference injection. The framework also uses an automated data annotation pipeline to address dat...

🔹 Publication Date: Published on Dec 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.03041
• PDF: https://arxiv.org/pdf/2512.03041
• Project Page: https://qinghew.github.io/MultiShotMaster/

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#VideoGeneration #GenerativeAI #DeepLearning #AI #ComputerVision

✨PSA: Pyramid Sparse Attention for Efficient Video Understanding and Generation

📝 Summary:
Pyramid Sparse Attention PSA introduces multi-level pooled key-value representations to overcome information loss in traditional sparse attention. It dynamically retains critical information, improving efficiency and performance for video understanding and generation tasks.

🔹 Publication Date: Published on Dec 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04025
• PDF: https://arxiv.org/pdf/2512.04025
• Project Page: https://ziplab.co/PSA/
• Github: https://github.com/ziplab/Pyramid-Sparse-Attention

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#SparseAttention #VideoUnderstanding #VideoGeneration #DeepLearning #ComputerVision

✨Reward Forcing: Efficient Streaming Video Generation with Rewarded Distribution Matching Distillation

📝 Summary:
Reward Forcing improves streaming video generation by using EMA-Sink to update context tokens, preventing static initial frames. It also introduces Rewarded Distribution Matching Distillation to prioritize dynamic content, enhancing motion quality and achieving state-of-the-art performance.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04678
• PDF: https://arxiv.org/pdf/2512.04678
• Project Page: https://reward-forcing.github.io/
• Github: https://reward-forcing.github.io/

🔹 Models citing this paper:
• https://huggingface.co/JaydenLu666/Reward-Forcing-T2V-1.3B

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#VideoGeneration #GenerativeAI #DeepLearning #ComputerVision #AIResearch

✨TV2TV: A Unified Framework for Interleaved Language and Video Generation

📝 Summary:
TV2TV is a unified framework for interleaved language and video generation, using a Mixture-of-Transformers. It learns to 'think in words' before 'acting in pixels,' enhancing visual quality, controllability, and prompt alignment. The model shows strong performance on video game and natural video...

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05103
• PDF: https://arxiv.org/pdf/2512.05103

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#VideoGeneration #GenerativeAI #MultimodalAI #Transformers #AI

✨Stable Video Infinity: Infinite-Length Video Generation with Error Recycling

📝 Summary:
Stable Video Infinity SVI generates infinite-length videos with high consistency and controllable stories. It introduces Error-Recycling Fine-Tuning, teaching the Diffusion Transformer to correct its self-generated errors and address the training-test discrepancy.

🔹 Publication Date: Published on Oct 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.09212
• PDF: https://arxiv.org/pdf/2510.09212
• Project Page: https://stable-video-infinity.github.io/homepage/
• Github: https://github.com/vita-epfl/Stable-Video-Infinity

🔹 Models citing this paper:
• https://huggingface.co/vita-video-gen/svi-model

✨ Datasets citing this paper:
• https://huggingface.co/datasets/vita-video-gen/svi-benchmark

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#VideoGeneration #AI #DiffusionModels #DeepLearning #ComputerVision

✨BulletTime: Decoupled Control of Time and Camera Pose for Video Generation

📝 Summary:
This paper presents a video diffusion framework that decouples scene dynamics from camera pose. This enables precise 4D control over time and viewpoint for high-quality video generation, outperforming prior models in controllability.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05076
• PDF: https://arxiv.org/pdf/2512.05076

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#VideoGeneration #DiffusionModels #GenerativeAI #ComputerVision #AICameraControl

✨EgoLCD: Egocentric Video Generation with Long Context Diffusion

📝 Summary:
EgoLCD addresses content drift in long egocentric video generation by integrating long-term sparse and attention-based short-term memory with narrative prompting. It achieves state-of-the-art perceptual quality and temporal consistency, mitigating generative forgetting.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04515
• PDF: https://arxiv.org/pdf/2512.04515
• Project Page: https://aigeeksgroup.github.io/EgoLCD/
• Github: https://github.com/AIGeeksGroup/EgoLCD

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#AI #VideoGeneration #DiffusionModels #ComputerVision #EgocentricVision

✨Generative Action Tell-Tales: Assessing Human Motion in Synthesized Videos

📝 Summary:
A new metric evaluates human action in generated videos by using a learned latent space of real-world actions, fusing skeletal geometry and appearance features. It significantly improves temporal and visual correctness assessment, outperforming existing methods and correlating better with human p...

🔹 Publication Date: Published on Dec 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01803
• PDF: https://arxiv.org/pdf/2512.01803
• Project Page: https://xthomasbu.github.io/video-gen-evals/
• Github: https://xthomasbu.github.io/video-gen-evals/

✨ Datasets citing this paper:
• https://huggingface.co/datasets/dghadiya/TAG-Bench-Video

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#VideoGeneration #HumanMotion #ComputerVision #AIMetrics #DeepLearning

✨Deep Forcing: Training-Free Long Video Generation with Deep Sink and Participative Compression

📝 Summary:
Deep Forcing is a training-free method that enhances real-time video diffusion for high-quality, long-duration generation. It uses Deep Sink for stable context and Participative Compression for efficient KV cache pruning, achieving over 12x extrapolation and improved consistency.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05081
• PDF: https://arxiv.org/pdf/2512.05081
• Github: https://cvlab-kaist.github.io/DeepForcing/

==================================

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#VideoGeneration #DiffusionModels #TrainingFreeAI #DeepLearning #ComputerVision

✨Light-X: Generative 4D Video Rendering with Camera and Illumination Control

📝 Summary:
Light-X is a video generation framework for controllable rendering from monocular videos with joint viewpoint and illumination control. It disentangles geometry and lighting using synthetic data for robust training, outperforming prior methods in both aspects.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05115
• PDF: https://arxiv.org/pdf/2512.05115
• Project Page: https://lightx-ai.github.io/
• Github: https://github.com/TQTQliu/Light-X

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#VideoGeneration #ComputerVision #AI #NeuralRendering #GenerativeAI

✨ProPhy: Progressive Physical Alignment for Dynamic World Simulation

📝 Summary:
ProPhy is a two-stage framework that enhances video generation by explicitly incorporating physics-aware conditioning and anisotropic generation. It uses a Mixture-of-Physics-Experts mechanism to extract fine-grained physical priors, improving physical consistency and realism in dynamic world sim...

🔹 Publication Date: Published on Dec 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.05564
• PDF: https://arxiv.org/pdf/2512.05564

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#VideoGeneration #PhysicsAI #DynamicSimulation #DeepLearning #ComputerVision

✨UnityVideo: Unified Multi-Modal Multi-Task Learning for Enhancing World-Aware Video Generation

📝 Summary:
UnityVideo is a unified framework enhancing video generation by integrating multiple modalities and training paradigms. It uses dynamic noising and a modality switcher for comprehensive world understanding. This improves video quality, consistency, and zero-shot generalization to new data.

🔹 Publication Date: Published on Dec 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07831
• PDF: https://arxiv.org/pdf/2512.07831
• Project Page: https://jackailab.github.io/Projects/UnityVideo/
• Github: https://github.com/dvlab-research/UnityVideo

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#VideoGeneration #MultimodalAI #GenerativeAI #DeepLearning #AIResearch

✨MIND-V: Hierarchical Video Generation for Long-Horizon Robotic Manipulation with RL-based Physical Alignment

📝 Summary:
MIND-V generates long-horizon, physically plausible robotic manipulation videos. This hierarchical framework uses semantic reasoning and an RL-based physical alignment strategy to synthesize robust, coherent actions, addressing data scarcity.

🔹 Publication Date: Published on Dec 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06628
• PDF: https://arxiv.org/pdf/2512.06628
• Project Page: https://github.com/Richard-Zhang-AI/MIND-V
• Github: https://github.com/Richard-Zhang-AI/MIND-V

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#Robotics #VideoGeneration #ReinforcementLearning #AI #MachineLearning

✨OneStory: Coherent Multi-Shot Video Generation with Adaptive Memory

📝 Summary:
OneStory generates coherent multi-shot videos by modeling global cross-shot context. It uses a Frame Selection module and an Adaptive Conditioner for next-shot generation, leveraging pretrained models and a new dataset. This achieves state-of-the-art narrative coherence for long-form video storyt...

🔹 Publication Date: Published on Dec 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.07802
• PDF: https://arxiv.org/pdf/2512.07802
• Project Page: https://zhaochongan.github.io/projects/OneStory/

==================================

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#VideoGeneration #AI #DeepLearning #ComputerVision #GenerativeAI

✨VideoSSM: Autoregressive Long Video Generation with Hybrid State-Space Memory

📝 Summary:
VideoSSM proposes a hybrid state-space memory model for long video generation. It unifies autoregressive diffusion with global state-space memory and local context to achieve state-of-the-art temporal consistency and motion stability. This enables scalable, interactive minute-scale video synthesis.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04519
• PDF: https://arxiv.org/pdf/2512.04519

==================================

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#VideoGeneration #GenerativeAI #DiffusionModels #StateSpaceModels #DeepLearning

✨GimbalDiffusion: Gravity-Aware Camera Control for Video Generation

📝 Summary:
GimbalDiffusion offers precise text-to-video camera control by using absolute, gravity-aligned coordinates. This framework defines interpretable camera trajectories, enhancing robustness and diverse motion beyond relative methods.

🔹 Publication Date: Published on Dec 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.09112
• PDF: https://arxiv.org/pdf/2512.09112
• Project Page: https://lvsn.github.io/GimbalDiffusion/

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#VideoGeneration #AI #DiffusionModels #ComputerVision #DeepLearning

✨Structure From Tracking: Distilling Structure-Preserving Motion for Video Generation

📝 Summary:
SAM2VideoX improves realistic video motion by distilling structure-preserving priors from a tracking model into a bidirectional diffusion model. It uses novel feature fusion and local alignment, achieving significant performance gains over prior methods.

🔹 Publication Date: Published on Dec 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11792
• PDF: https://arxiv.org/pdf/2512.11792
• Project Page: https://sam2videox.github.io/

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#VideoGeneration #DiffusionModels #ComputerVision #DeepLearning #MotionTracking

✨EgoX: Egocentric Video Generation from a Single Exocentric Video

📝 Summary:
EgoX generates egocentric videos from single exocentric inputs. It uses video diffusion models with LoRA adaptation, unified conditioning, and geometry-guided self-attention for coherent and realistic results.

🔹 Publication Date: Published on Dec 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08269
• PDF: https://arxiv.org/pdf/2512.08269
• Project Page: https://keh0t0.github.io/EgoX/
• Github: https://github.com/KEH0T0/EgoX

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#EgocentricVideo #VideoGeneration #DiffusionModels #ComputerVision #DeepLearning