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21 of 94 articles
The Matrix - video diffusion with real time inputÂ
Infinite-Horizon World Generation with Real-Time Interaction
Interactive Point Cloud Latent Diffusion for 3D Generation.
While 3D content generation has advanced significantly, existing methods still face challenges with input formats, latent space design, and output representations. This paper introduces a novel 3D generation framework that addresses these challenges, offering scalable, high-quality 3D generation with an interactive Point Cloud-structured Latent space. Our framework employs a Variational Autoencoder (VAE) with multi-view posed RGB-D(epth)-N(ormal) renderings as input, using a unique latent space design that preserves 3D shape information, and incorporates a cascaded latent diffusion model for improved shape-texture disentanglement. The proposed method, GaussianAnything, supports multi-modal conditional 3D generation, allowing for point cloud, caption, and single/multi-view image inputs. Notably, the newly proposed latent space naturally enables geometry-texture disentanglement, thus allowing 3D-aware editing. Experimental results demonstrate the effectiveness of our approach on multiple datasets, outperforming existing methods in both text- and image-conditioned 3D generation.
LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models
LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models
Introducing Multimodal Llama 3.2
Complete this Guided Project in under 2 hours. Join our new short course, Introducing Multimodal Llama 3.2, and learn from Amit Sangani, Senior Director of…
PlayCanvas - SuperSplatÂ
Collaboratively build stunning HTML5 visualizations and games
The Effect of Sampling Temperature on Problem Solving in Large Language Models
In this research study, we empirically investigate the effect of sampling temperature on the performance of Large Language Models (LLMs) on various problem-solving tasks. We created a multiple-choice question-and-answer (MCQA) exam by randomly sampling problems from standard LLM benchmarks. Then, we used nine popular LLMs with five prompt-engineering techniques to solve the MCQA problems while increasing the sampling temperature from 0.0 to 1.6. Despite anecdotal reports to the contrary, our empirical results indicate that changes in temperature from 0.0 to 1.0 do not have a statistically significant impact on LLM performance for problem-solving tasks. In addition, these results appear to generalize across LLMs, prompt-engineering techniques, and problem domains. All code, data, and supplemental materials are available on GitHub at: https://github.com/matthewrenze/jhu-llm-temperature
GitHub - Tencent/Hunyuan3D-1
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IBM’s Granite foundation model: A detailed look at its training data
While many AI model developers publicly release research papers and their data training approaches, we’ll focus on one model in particular– IBM’s Granite model, where IBM has gone one step further and released their specific training data.
Why AI writing detectors don’t work
Can AI writing detectors be trusted? We dig into the theory behind them.