✨ShotStream: Streaming Multi-Shot Video Generation for Interactive Storytelling

📝 Summary:
ShotStream enables real-time interactive multi-shot video generation via a novel causal architecture. It uses dual-cache memory for visual consistency and two-stage distillation to reduce latency and error. This achieves high-quality, coherent videos at 16 FPS, paving the way for dynamic storytel...

🔹 Publication Date: Published on Mar 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25746
• PDF: https://arxiv.org/pdf/2603.25746
• Project Page: https://luo0207.github.io/ShotStream/
• Github: https://github.com/KlingAIResearch/ShotStream

🔹 Models citing this paper:
• https://huggingface.co/KlingTeam/ShotStream

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#VideoGeneration #GenerativeAI #RealTimeAI #DeepLearning #AIStorytelling

✨PackForcing: Short Video Training Suffices for Long Video Sampling and Long Context Inference

📝 Summary:
PackForcing enables efficient, long-video generation via hierarchical KV-cache management and spatiotemporal compression, overcoming memory and consistency issues. It generates 2-minute coherent videos on a single GPU, demonstrating that short-video training suffices for high-quality long-video s...

🔹 Publication Date: Published on Mar 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25730
• PDF: https://arxiv.org/pdf/2603.25730
• Github: https://github.com/ShandaAI/PackForcing

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#VideoGeneration #GenerativeAI #DeepLearning #ModelEfficiency #LongContext

✨TokenDial: Continuous Attribute Control in Text-to-Video via Spatiotemporal Token Offsets

📝 Summary:
TokenDial enables precise attribute control in text-to-video models by using additive offsets in spatiotemporal token space for coherent edits without retraining. AI-generated summary We present Token...

🔹 Publication Date: Published on Mar 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.27520
• PDF: https://arxiv.org/pdf/2603.27520
• Project Page: https://tokendial.github.io/
• Github: https://github.com/ariannaliu/TokenDial

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#TextToVideo #GenerativeAI #AIControl #VideoGeneration #DeepLearning

✨DynaVid: Learning to Generate Highly Dynamic Videos using Synthetic Motion Data

📝 Summary:
DynaVid improves dynamic video synthesis by training with synthetic optical flow, which provides diverse motion patterns without artificial appearances. A two-stage framework learns dynamic motion while preserving visual realism, enhancing motion control.

🔹 Publication Date: Published on Apr 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01666
• PDF: https://arxiv.org/pdf/2604.01666
• Project Page: https://jinwonjoon.github.io/DynaVid/

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#VideoGeneration #AIVideo #DeepLearning #ComputerVision #SyntheticData

✨Salt: Self-Consistent Distribution Matching with Cache-Aware Training for Fast Video Generation

📝 Summary:
The paper introduces Salt, a method for fast video generation. It proposes Self-Consistent Distribution Matching Distillation SC-DMD to improve low-NFE quality by regularizing denoising updates. Cache-Distribution-Aware training further optimizes real-time autoregressive generation using KV cache.

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03118
• PDF: https://arxiv.org/pdf/2604.03118
• Github: https://github.com/XingtongGe/Salt

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#VideoGeneration #GenerativeAI #DeepLearning #AIResearch #RealTimeAI

✨Matrix-Game 3.0: Real-Time and Streaming Interactive World Model with Long-Horizon Memory

📝 Summary:
Matrix-Game 3.0 is a memory-augmented diffusion model achieving real-time 720p interactive video generation with long-term temporal consistency. It uses an advanced data engine, a self-correction training framework with memory, and efficient inference strategies. This enables practical, industria...

🔹 Publication Date: Published on Apr 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.08995
• PDF: https://arxiv.org/pdf/2604.08995
• Project Page: https://matrix-game-v3.github.io/

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#DiffusionModels #VideoGeneration #RealTimeAI #GenerativeAI #MachineLearning

✨CT-1: Vision-Language-Camera Models Transfer Spatial Reasoning Knowledge to Camera-Controllable Video Generation

📝 Summary:
CT-1 is a Vision-Language-Camera model that improves camera-controllable video generation. It uses a Diffusion Transformer and Wavelet Regularization Loss to accurately estimate camera trajectories, enabling precise video synthesis. This achieves 25.7% better accuracy than prior methods.

🔹 Publication Date: Published on Apr 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.09201
• PDF: https://arxiv.org/pdf/2604.09201
• Project Page: https://gulucaptain.github.io/Camera-Transformer-1/
• Github: https://github.com/gulucaptain/Camera-Transformer-1

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#AI #VideoGeneration #ComputerVision #DiffusionModels #VisionLanguageModels

✨Uni-ViGU: Towards Unified Video Generation and Understanding via A Diffusion-Based Video Generator

📝 Summary:
Uni-ViGU introduces a unified framework for video generation and understanding, uniquely building upon a video generator as its foundation. It uses unified flow matching and a bidirectional training mechanism to achieve competitive performance in both generation and understanding tasks.

🔹 Publication Date: Published on Apr 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.08121
• PDF: https://arxiv.org/pdf/2604.08121
• Project Page: https://fr0zencrane.github.io/uni-vigu-page/
• Github: https://fr0zencrane.github.io/uni-vigu-page/

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#VideoGeneration #VideoUnderstanding #DiffusionModels #AIResearch #DeepLearning

✨CityRAG: Stepping Into a City via Spatially-Grounded Video Generation

📝 Summary:
CityRAG generates long-term, physically grounded video sequences that maintain environmental consistency and support complex navigation through real-world geography using geo-registered data as contex...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19741
• PDF: https://arxiv.org/pdf/2604.19741
• Project Page: https://cityrag.github.io/

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#VideoGeneration #GenerativeAI #SpatialAI #ComputerVision #UrbanSimulation

🔥 Warp-as-History: Generalizable Camera-Controlled Video Generation from One Training Video

💡 The paper proposes a novel approach called Warp-as-History for camera-controlled video generation. Existing methods for this task typically require large-scale camera-annotated videos for post-training or rely on test-time optimization, which can be time-consuming and costly. The proposed method addresses this problem by transforming camera-induced warps into pseudo-history representations, which enables a frozen video generation model to follow camera trajectories without any training or test-time optimization.

The Warp-as-History method works by constructing camera-warped pseudo-history from past observations and feeding it through the model's visual-history pathway. The positional encoding is aligned with the target frames being denoised, and warped-history tokens without valid source observations are removed. This simple interface reveals a non-trivial zero-shot capability of the model to follow camera trajectories.

The results show that the proposed method can achieve good camera adherence, visual quality, and motion dynamics without requiring large-scale camera-annotated videos or test-time optimization. Furthermore, lightweight offline finetuning on only one camera-annotated video can further improve the model's capability and generalize to unseen videos. Extensive experiments on diverse datasets confirm the effectiveness of the Warp-as-History method, making it a promising approach for camera-controlled video generation.

Overall, the paper's contributions include a novel method for camera-controlled video generation that requires minimal training data and no test-time optimization, and demonstrates the potential for zero-shot capability in video generation models. The proposed approach has the potential to simplify the process of camera-controlled video generation and make it more accessible to a wider range of applications.

📅 Published on May 14

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2605.15182
• PDF: https://arxiv.org/pdf/2605.15182
• Project Page: https://yyfz.github.io/warp-as-history/

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📢 By: https://xn--r1a.website/PaperNexus

#VideoGeneration #CameraControlledSynthesis #WarpAsHistory #PseudoHistoryRepresentations #CameraInducedWarps

Shock: A Chinese laboratory has put half of the paid video generation industry in an awkward position. 😲

You upload a photo and an audio recording — and you get a talking avatar with lip synchronization, which can maintain quality for several minutes. And all this with open source code. 🤖

What used to require a camera and editing now increasingly resembles a GitHub repository. 💻
It's called LongCat-Avatar. 🐱

GitHub - https://github.com/meituan-longcat/LongCat-Video
Weights - https://huggingface.co/meituan-longcat/LongCat-Video-Avatar-1.5

#AI #OpenSource #VideoGeneration #LongCat #TechNews #Innovation

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🔥 VideoCoCo: Code-as-CoT for Physically-Consistent Video Generation via an Agentic Dual-Engine System

💡 The paper introduces VideoCoCo, a novel framework for physically consistent video generation. The problem addressed is that existing text-to-video models struggle to generate videos with physically consistent dynamics, as they must infer the temporal evolution of a scene implicitly from a highly compressed text prompt. Current chain-of-thought approaches introduce intermediate plans or visual states, but these representations are typically non-executable or temporally sparse, limiting their ability to instantiate and control the complete spatiotemporal process.

To address this limitation, VideoCoCo uses an agentic dual-engine system, where executable Blender code serves as a process-level chain of thought. Given a text prompt, a coding agent synthesizes a Blender program that explicitly specifies the scene and its temporal evolution. The executable simulation engine runs the program to produce a deterministic spatiotemporal draft, which is then transformed into a photorealistic video by a generative video engine through draft-conditioned editing.

The authors also construct VideoCoCo-3K, a curated dataset of draft-instruction-target triplets to adapt the video editor to simulated drafts. The results demonstrate that VideoCoCo improves the OmniWeaving baseline from 0.475 to 0.558 on PhyGenBench and from 52.18 to 77.88 on VBench-2.0, achieving the best average score on both benchmarks. The findings show that executable code provides an effective, controllable, and inspectable intermediate representation for physically consistent video generation.

📅 Published on Jul 29

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.27380
• PDF: https://arxiv.org/pdf/2607.27380

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#VideoGeneration #PhysicallyConsistentModeling #CodeAsCoT #AgenticDualEngine #TextToVideoSynthesis