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Publications
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View-Dependent Adaptive Cloth Simulation with Buckling Compensation
IEEE Transactions on Visualization and Computer Graphics
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View-Dependent Adaptive Cloth Simulation
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
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Near-exhaustive Precomputation of Secondary Cloth Effects
ACM Transactions on Graphics (Proc. SIGGRAPH 2013)
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Samet Ozkale
Video-As-Prompt: Unified Semantic Control for Video Generation Just came across one of the most exciting research papers in video generation lately — by Yuxuan Bian, Xin Chen, Zenan Li, Tiancheng Zhi, Shen Sang, Linjie Luo, and Qiang Xu. Achieves state-of-the-art results with 38.7% user preference rate, rivaling commercial models, and includes 100K+ paired video dataset. Instead of giving text prompts or rigid structure maps, what if you could guide an AI model just by showing it another video? That’s exactly what Video-As-Prompt does. It treats a reference video as a “semantic prompt.” Basically, it lets the model understand and transfer meaning from one video to another, without hard-coded rules or special training for every scenario. Why does this matter? Because semantic control is the missing piece for true creative direction in video AI. We’ve had strong generators, but fine-tuned, generalized control, the ability to say, “make it look like this, feel like that,” has always been the bottleneck. It opens new doors for film, gaming, education, and all kinds of content creation where meaning, not just motion, matters. Let me know which topics you would like to see new papers on, via comments or messages.
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RASEL SARKER
🛠 Tech Stack & Skills: ~Streamlit – Interactive UI development ~LangChain – Retrieval-Augmented Generation (RAG) pipeline design ~FAISS – Vector database for semantic search ~Google Gemini Pro 2.5 – Context-aware LLM integration ~SentenceTransformers – Text embeddings generation ~PyPDF2 – PDF text extraction ~Python – End-to-end system development ~LLM Prompt Engineering – Optimizing responses for domain-specific queries ~Document Parsing & Chunking – Preparing structured context for retrieval ~Semantic Search Optimization – Improving relevance of retrieved chunks 🔗 GitHub Repo: https://lnkd.in/gPQKzHp4
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Peter Loleit
This new research paper debuts an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that supports both thinking and direct response modes. This model is optimised towards better function in Agents for interacting with tools and externally, complex Reasoning for solving multi-step problems in domains like mathematics and science; and Coding skills for software engineering tasks. Nice to see advances in Open Source Models that are generally available for the Community and Businesses.
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