@_akhaliq Thank you very much for featuring our work!
We’re excited to share that it has just been accepted to NeurIPS 2025.
The code & model are now open-sourced here: https://t.co/str9Aq6N8o
The website for a quick summary of the work: https://t.co/KJpGoRztYb
🚀 Thrilled to share our work **CineScene: Implicit 3D as Effective Scene Representation for Cinematic Video Generation**, selected as a **CVPR 2026 Highlight**! 🏆
Video generation currently faces a dilemma:
❌ **2D Diffusion**: Lacks spatial consistency in large camera moves.
❌ **Explicit 3D**: Complex, slow, and prone to reconstruction artifacts.
We bridge this gap by using **Implicit 3D as a Spatial Anchor**, using this representation as **context** into the video generation model. ⚓️
Through **Scene-Decoupled Diffusion**, we represent the static environment via implicit 3D features, decoupling scene priors from dynamic motion. This enables unprecedented scene consistency and precise control over complex scenes, camera paths, and characters. 🎬
CineScene is a versatile toolkit for the future of filmmaking & World Models:
📍 **Virtual Stage**: High-fidelity, consistent environments for virtual production.
🎬 **Scene Blocking**: Directorial control over scene background, camera paths, and prompt-driven foreground character dynamics.
🌍 **World Simulators**: A potential step towards stable, consistent world modeling.
We are also excited to open-source the **Scene-Decoupled Video Dataset**, a large-scale, high-quality collection to empower the community! 🎁
🔗 Project: [https://t.co/ENjoQDdQQf](https://t.co/6tnZY9IbSz)
📊 Dataset: [https://t.co/h7PVoVR97Z](https://t.co/cAWp3P7E0l)
📄 ArXiv: [https://t.co/2c7e38rCCZ](https://t.co/bs5jXAucpc)
Huge thanks to the amazing co-authors! 🙏 @KaiyiHUANG84276@xinntao@yukun6414@yujiwenHK@jianhongbai@lin_zinan@FiNingm@wanfufeng
\#CVPR2026 #AIvideo #GenerativeAI #FilmGeneration #WorldModels #ComputerVision @CVPR
BlueCodeAgent is an end-to-end blue-teaming framework built to boost code security using automated red-teaming processes, data, and safety rules to guide LLMs’ defensive decisions. Dynamic testing reduces false positives in vulnerability detection: https://t.co/BMzhbtMf8e
Code agents help streamline software development workflows, but may also introduce critical security risks. Learn how RedCodeAgent automates and improves “red-teaming” attack simulations to help uncover real-world threats that other methods overlook: https://t.co/pgSbW9um6Z
@Kangwook_Lee In Section 3.1 of our paper, https://t.co/YYzWU6WgtW, we gave theoretical justification and examples for why parallel generation could fail
Super cool! Really innovative use of Private Evolution votes as the RL reward to fine-tune LLMs — a big step forward for DP synthetic data. Congrats, @hou_char!
Gave a talk at @OpenAI on our work 🌸 POPri “Policy Optimization for Private Data”. POPri is a huge improvement in synthetic data generation under security+privacy constraints! Learn more:
@StatMLPapers Thank you very much for sharing our work!
For readers:
We’re excited to share that it has just been accepted to NeurIPS 2025.
The code & model are now open-sourced here: https://t.co/str9Aq6N8o
The website for a quick summary of the work: https://t.co/KJpGoRztYb
@HuggingPapers Thank you very much for sharing our work!
For readers:
We’re excited to share that it has just been accepted to NeurIPS 2025.
The code & model are now open-sourced here: https://t.co/str9Aq6N8o
The website for a quick summary of the work: https://t.co/KJpGoRztYb
@fly51fly Thank you very much for sharing our work!
For readers:
We’re excited to share that it has just been accepted to NeurIPS 2025.
The code & model are now open-sourced here: https://t.co/str9Aq6N8o
The website for a quick summary of the work: https://t.co/KJpGoRztYb
@StatsPapers Thank you very much for sharing our work!
For readers:
We’re excited to share that it has just been accepted to NeurIPS 2025.
The code & model are now open-sourced here: https://t.co/str9Aq6N8o
The website for a quick summary of the work: https://t.co/KJpGoRztYb
@aimodelsfyi Thank you very much for sharing our work!
For readers:
We’re excited to share that it has just been accepted to NeurIPS 2025.
The code & model are now open-sourced here: https://t.co/str9Aq6N8o
The website for a quick summary of the work: https://t.co/KJpGoRztYb
@AINativeF_zh Thank you very much for sharing our work!
For readers:
We’re excited to share that it has just been accepted to NeurIPS 2025.
The code & model are now open-sourced here: https://t.co/str9Aq6N8o
The website for a quick summary of the work: https://t.co/KJpGoRztYb
@AINativeF Thank you very much for sharing our work!
For readers:
We’re excited to share that it has just been accepted to NeurIPS 2025.
The code & model are now open-sourced here: https://t.co/str9Aq6N8o
The website for a quick summary of the work: https://t.co/KJpGoRztYb
@nojobafterphoto@Microsoft Thank you very much for sharing our work!
For readers:
We’re excited to share that it has just been accepted to NeurIPS 2025.
The code & model are now open-sourced here: https://t.co/str9Aq6N8o
The website for a quick summary of the work: https://t.co/KJpGoRztYb
researchers really said what if we made ONE model that does everything and called it latent zoning like theyre planning a neighborhood 🏘️ but honestly the ambition of trying to unify generative modeling, representation learning AND classification is...
https://t.co/7yWEPFUqho
Microsoft introduces Latent Zoning Network (LZN)
A unified principle for generative modeling, representation learning, and classification. LZN uses a shared Gaussian latent space and modular encoders/decoders to tackle all three core ML problems at once!
[LG] Latent Zoning Network: A Unified Principle for Generative Modeling, Representation Learning, and Classification
Z Lin, E Liu, X Ning, J Zhu... [Microsoft Research & Tsinghua University] (2025)
https://t.co/wKtjTpUQit
@_akhaliq Thank you very much for featuring our work!
For readers:
We’re excited to share that it has just been accepted to NeurIPS 2025.
The code & model are now open-sourced here: https://t.co/str9Aq6N8o
The website for a quick summary of the work: https://t.co/KJpGoRztYb