Name: Diffusion Models - Connect Session
Start: 2025-11-24T09:00:00-08:00
End: 2025-11-24T10:00:00-08:00

[ResearchTrend.AI](http://researchtrend.ai/) Diffusion Model Connect Session: 3D Video & Geometric Foundations!

We are excited to announce our upcoming biweekly Diffusion Model (DiffM) Connect Session on [ResearchTrend.AI](https://www.linkedin.com/company/researchtrend-ai/)!

This virtual session 💻 features two presentations from leading researchers 🧑‍🔬, diving deep into the technical advancements and theoretical underpinnings of Diffusion Models.

Agenda (UTC) - Monday, November 24th

08:00 - 08:30: [Geonung Kimg](https://www.linkedin.com/in/kimgeonung/)
📄 Paper: VideoFrom3D: 3D Scene Video Generation via Complementary Image and Video Diffusion Models
💡 Abstract: Generating high-fidelity 3D scene videos is challenging due to the difficulty of jointly modeling visual quality, motion, and temporal consistency. Geonung will present VideoFrom3D, a novel framework that uses the complementary strengths of image and video diffusion models. It synthesizes high-quality, style-consistent videos from coarse 3D geometry and a camera path, streamlining 3D graphic design workflows without needing paired 3D/natural image datasets.

08:30 - 09:00: Xiang Li
📄 Paper: When Scores Learn Geometry: Rate Separations under the Manifold Hypothesis
💡 Abstract: Score-based methods (like diffusion models) are usually viewed as learning the full data distribution. Xiang will propose an alternative, groundbreaking perspective: their success comes from implicitly learning the data manifold (geometry). He reveals a sharp separation of scales, showing that learning the geometry is O(σ−2) stronger than learning the distribution, suggesting a paradigm shift from demanding distributional learning to more robust geometric learning.

🌟 This is a fantastic opportunity to engage directly with new, high-impact research at the frontier of generative AI and its theoretical limits.

🗓️ Time: 8:00 AM - 9:00 AM UTC
📍 Location: Virtual
👉 Register for this event here: [https://lnkd.in/eM5wPGHK](https://lnkd.in/eM5wPGHK)

Don't miss our future sessions! 📆 Find out more about upcoming events: [https://lnkd.in/g7-iczUp](https://lnkd.in/g7-iczUp)

[hashtag#DiffusionModels](https://www.linkedin.com/search/results/all/?keywords=%23diffusionmodels&origin=HASH_TAG_FROM_FEED) [hashtag#GenerativeAI](https://www.linkedin.com/search/results/all/?keywords=%23generativeai&origin=HASH_TAG_FROM_FEED) [hashtag#3DGraphics](https://www.linkedin.com/search/results/all/?keywords=%233dgraphics&origin=HASH_TAG_FROM_FEED) [hashtag#VideoGeneration](https://www.linkedin.com/search/results/all/?keywords=%23videogeneration&origin=HASH_TAG_FROM_FEED) [hashtag#AIResearch](https://www.linkedin.com/search/results/all/?keywords=%23airesearch&origin=HASH_TAG_FROM_FEED) [hashtag#MachineLearning](https://www.linkedin.com/search/results/all/?keywords=%23machinelearning&origin=HASH_TAG_FROM_FEED) [hashtag#DeepLearning](https://www.linkedin.com/search/results/all/?keywords=%23deeplearning&origin=HASH_TAG_FROM_FEED) [hashtag#Webinar](https://www.linkedin.com/search/results/all/?keywords=%23webinar&origin=HASH_TAG_FROM_FEED) [hashtag#VirtualEvent](https://www.linkedin.com/search/results/all/?keywords=%23virtualevent&origin=HASH_TAG_FROM_FEED) [hashtag#GeometricLearning](https://www.linkedin.com/search/results/all/?keywords=%23geometriclearning&origin=HASH_TAG_FROM_FEED) [hashtag#ScoreBasedModels](https://www.linkedin.com/search/results/all/?keywords=%23scorebasedmodels&origin=HASH_TAG_FROM_FEED) [hashtag#ManifoldHypothesis](https://www.linkedin.com/search/results/all/?keywords=%23manifoldhypothesis&origin=HASH_TAG_FROM_FEED)

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